diff --git "a/widesearch.jsonl" "b/widesearch.jsonl"
--- "a/widesearch.jsonl"
+++ "b/widesearch.jsonl"
@@ -1,11 +1,11 @@
-{"instance_id":"ws_en_001","query":"My son is about to start his university applications but he’s still uncertain about both his major and which universities to apply to. Could you help me find the top five universities in each of the five broad subjects from the QS World University Rankings by Subject 2025, and also check their standings in the QS World University Rankings 2025 and the Times Higher Education World University Rankings 2025? And I need the home page of the university's official website, standard application deadline for regular decision as well as the application fee without the fee waiver.\n\nPlease organize the results in one Markdown table with the following columns:\nSubject, University, QS World University Rankings by Subject 2025, QS World University Rankings 2025, Times Higher Education  World University Rankings 2025, Home Page, Application Deadline, Application Fee\nPlease use the universities’ full official names in English. \nUse only Arabic numerals in the ranking, for example: 1.\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is \n```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"subject\", \"university\"], \"required\": [\"subject\", \"university\", \"qsworlduniversityrankingsbysubject2025\", \"qsworlduniversityrankings2025\", \"timeshighereducationworlduniversityrankings2025\", \"homepage\", \"applicationdeadline\", \"applicationfee\"], \"eval_pipeline\": {\"applicationdeadline\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\nThe month and day must be correct\"}, \"applicationfee\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\nIf there are multiple fees in the reference answer, all must be included.\"}, \"homepage\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"url_match\"]}, \"subject\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"university\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"qsworlduniversityrankingsbysubject2025\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"qsworlduniversityrankings2025\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"timeshighereducationworlduniversityrankings2025\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"en"}
-{"instance_id":"ws_en_002","query":"I’m currently mapping the product portfolios of several spirits brands including Johnnie Walker, Chivas Regal, Smirnoff, Grey Goose, Absolut Vodka, Bacardi as of June 2025.\nThe scope covers only their standard product in the Core \/ Permanent Range excluding any flavor variants or limited-edition\/seasonal releases.\nPlease organize the results in one Markdown table with the columns: Brand, Product, Category, Sub-category, Pack Size (Bottle), ABV %. For any missing information, fill in '\/'.\nPlease ensure that no cells in the “Brand” column are left blank.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"brand\", \"product\"], \"required\": [\"brand\", \"product\", \"category\", \"sub-category\", \"packsize(bottle)\", \"abv%\"], \"eval_pipeline\": {\"brand\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"packsize(bottle)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\nDifferent regions have wines of different specifications, so the capacity does not need to exactly match the reference answer\"}, \"category\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"sub-category\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\nThe answer to be evaluated can be broader than the reference answer.\"}, \"product\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"abv%\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
-{"instance_id":"ws_en_003","query":"Could you help compile a detailed dataset of Eileen Gu's competitive achievements in freestyle skiing, including the following information: event dates (year and month), specific event names (e.g., Winter Olympic Games, FIS Freestyle Ski World Championships, FIS Freestyle Ski World Cup stops, Winter Youth Olympic Games, X Games), event levels (e.g., Olympic, World Championship, World Cup, Youth Olympic, invitational, etc.), disciplines (women's big air, women's slopestyle, women's halfpipe, etc.), and results (e.g., gold medal, the 4th, etc.), and the top 3 players? By the way, as for competitions, only official matches should be counted, preliminary qualifiers should not be included.\n\nPlease organize the results in one Markdown table with columns labeled: Date, Event Name, Level, Discipline, Result, Top 3 Players.\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"date\", \"discipline\", \"eventname\"], \"required\": [\"date\", \"eventname\", \"level\", \"discipline\", \"result\", \"top3players\"], \"eval_pipeline\": {\"date\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"eventname\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"level\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\nThere is no clear wording for the competition levels of X Games or Dew Tour, so any reasonable wording is acceptable.\"}, \"discipline\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"result\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"top3players\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
-{"instance_id":"ws_en_004","query":"Could you help me compile a list of all base models from Samsung's Galaxy S Series and Galaxy Note Series smartphones released in the U.S. market between January 2015 and May 2025 (including January 2015 and May 2025 )? Please include the following details for each model.\n\nNotes:\nMarket Restriction: Only include models officially released in the U.S. market (exclude region-exclusive models limited to specific countries\/regions).\nBase Model Definition: Refers to models without suffixes (common suffixes: Ultra, Plus, FE, 5G, Z, Flip, Fold, \"Edition\", etc.). For a model lineup, select the version with the lowest launch price (e.g., \"Galaxy S23\" with 8GB+128GB). Note: \"5G\" and \"4G\" are not considered suffixes.\n4G\/5G Preference: If a base model has both 4G and 5G versions, only include the 5G version.\nRelease Date: The time when it was first released in the U.S..\nFormatting Rules:\nRelease Date: yyyy-mm-dd\nPre-installed OS: Android\/iOS number (e.g., Android 5)\nResolution: \"numberxnumber\" (e.g., 2340x1080)\nCPU Manufacturing Process: \"number+unit\" (e.g., 4nm)\nCPU Core Count: \"x+core\" (e.g., 8-core)\nHighest Camera Resolution: \"number+unit\" (e.g., 200MP)\nCPU Model: Omit the processor brand (e.g., only \"Snapdragon 8 Gen 2\" instead of \"Qualcomm Snapdragon 8 Gen 2\")\n\nPlease organize the results in one Markdown table with the specified column order:\nModel Name, Release Date, Pre-installed OS, Processor Brand, CPU Model, CPU Manufacturing Process, CPU Core Count, Screen Size, Resolution, Battery Capacity, RAM Size, Storage Size, Highest Camera Resolution, Launch Price (USD)\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"releasedate\"], \"required\": [\"modelname\", \"releasedate\", \"pre-installedos\", \"processorbrand\", \"cpumodel\", \"cpumanufacturingprocess\", \"cpucorecount\", \"screensize\", \"resolution\", \"batterycapacity\", \"ramsize\", \"storagesize\", \"highestcameraresolution\", \"launchprice(usd)\"], \"eval_pipeline\": {\"releasedate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"processorbrand\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"cpumanufacturingprocess\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"cpucorecount\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"batterycapacity\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"ramsize\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"storagesize\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"highestcameraresolution\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"launchprice(usd)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"screensize\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"resolution\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"modelname\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\nDo not need to be entirely strictly consistent\"}, \"cpumodel\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\njust provide the core part, 'for Samsung' is optional.\"}, \"pre-installedos\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
+{"instance_id":"ws_en_001","query":"My son is about to start his university applications in 2025 for postgraduates but he’s still uncertain about both his major and which universities to apply to. Could you help me find the top five universities in each of the five broad subjects from the QS World University Rankings by Subject 2025, and also check their standings in the QS World University Rankings 2025 and the Times Higher Education World University Rankings 2025? And I need the home page of the university's official website, standard application deadline for regular decision as well as the application fee without the fee waiver.\n\nPlease organize the results in one Markdown table with the following columns:\nSubject, University, QS World University Rankings by Subject 2025, QS World University Rankings 2025, Times Higher Education  World University Rankings 2025, Home Page, Application Deadline, Application Fee\nPlease use the universities’ full official names in English. \nUse only Arabic numerals in the ranking, for example: 1.\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is \n```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"subject\", \"university\"], \"required\": [\"subject\", \"university\", \"qsworlduniversityrankingsbysubject2025\", \"qsworlduniversityrankings2025\", \"timeshighereducationworlduniversityrankings2025\", \"homepage\", \"applicationdeadline\", \"applicationfee\"], \"eval_pipeline\": {\"applicationdeadline\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\nThe month and day must be correct\"}, \"applicationfee\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\nIf there are multiple fees in the reference answer, all must be included.\"}, \"homepage\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"url_match\"]}, \"subject\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"university\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"qsworlduniversityrankingsbysubject2025\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"qsworlduniversityrankings2025\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"timeshighereducationworlduniversityrankings2025\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"en"}
+{"instance_id":"ws_en_002","query":"I’m currently mapping the product portfolios of several spirits brands including Johnnie Walker, Chivas Regal, Smirnoff, Grey Goose, Absolut Vodka, Bacardi as of June 2025.\nThe scope covers only their standard product in the Core \/ Permanent Range excluding any flavor variants or limited-edition\/seasonal releases.\nPlease organize the results in one Markdown table with the columns: Brand, Product, Category, Sub-category, Pack Size (Bottle), ABV %. For any missing information, fill in '\/'.\nPlease ensure that no cells in the “Brand” column are left blank.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"brand\", \"product\"], \"required\": [\"brand\", \"product\", \"category\", \"sub-category\", \"packsize(bottle)\", \"abv%\"], \"eval_pipeline\": {\"brand\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"packsize(bottle)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\nDifferent regions have wines of different specifications, so the capacity does not need to exactly match the reference answer\"}, \"category\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"sub-category\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\nThe answer to be evaluated can be broader than the reference answer, for vodka, both NA or premium vodka is accepted.\"}, \"product\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"abv%\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
+{"instance_id":"ws_en_003","query":"Could you help compile a detailed dataset of Eileen Gu's competitive achievements in freestyle skiing from Jan, 2015 to Jan 31st 2025, including the following information: event dates (year and month), specific event names (e.g., Winter Olympic Games, FIS Freestyle Ski World Championships, FIS Freestyle Ski World Cup stops, Winter Youth Olympic Games, X Games), event levels (e.g., Olympic, World Championship, World Cup, Youth Olympic, invitational, etc.), disciplines (women's big air, women's slopestyle, women's halfpipe, etc.), and results (e.g., gold medal, the 4th, etc.), and the top 3 players? By the way, as for competitions, only official matches should be counted, preliminary qualifiers should not be included.\n\nPlease organize the results in one Markdown table with columns labeled: Date, Event Name, Level, Discipline, Result, Top 3 Players.\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"date\", \"discipline\", \"eventname\"], \"required\": [\"date\", \"eventname\", \"level\", \"discipline\", \"result\", \"top3players\"], \"eval_pipeline\": {\"date\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"eventname\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"level\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\nThere is no clear wording for the competition levels of X Games or Dew Tour, so any reasonable wording is acceptable.\"}, \"discipline\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"result\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"top3players\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
+{"instance_id":"ws_en_004","query":"Could you help me compile a list of all base models from Samsung's Galaxy S Series and Galaxy Note Series smartphones released in the U.S. market between January 2015 and May 2025 (including January 2015 and May 2025 )? Please include the following details for each model.\n\nNotes:\nMarket Restriction: Only include models officially released in the U.S. market (exclude region-exclusive models limited to specific countries\/regions).\nBase Model Definition: Refers to models without suffixes (common suffixes: Ultra, Plus, FE, 5G, Z, Flip, Fold, \"Edition\", etc.). For a model lineup, select the version with the lowest launch price (e.g., \"Galaxy S23\" with 8GB+128GB). Note: \"5G\" and \"4G\" are not considered suffixes.\n4G\/5G Preference: If a base model has both 4G and 5G versions, only include the 5G version.\nRelease Date: The time when it was first released in the U.S..\nFormatting Rules:\nRelease Date: yyyy-mm-dd\nPre-installed OS: Android\/iOS number (e.g., Android 5)\nResolution: \"numberxnumber\" (e.g., 2340x1080)\nCPU Manufacturing Process: \"number+unit\" (e.g., 4nm)\nCPU Core Count: \"x+core\" (e.g., 8-core)\nHighest Camera Resolution: \"number+unit\" (e.g., 200MP)\nCPU Model: Omit the processor brand (e.g., only \"Snapdragon 8 Gen 2\" instead of \"Qualcomm Snapdragon 8 Gen 2\")\nstorage: refer to the lowest storage of the base model.\n\nPlease organize the results in one Markdown table with the specified column order:\nModel Name, Release Date, Pre-installed OS, Processor Brand, CPU Model, CPU Manufacturing Process, CPU Core Count, Screen Size, Resolution, Battery Capacity, RAM Size, Storage Size, Highest Camera Resolution, Launch Price (USD)\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"releasedate\"], \"required\": [\"modelname\", \"releasedate\", \"pre-installedos\", \"processorbrand\", \"cpumodel\", \"cpumanufacturingprocess\", \"cpucorecount\", \"screensize\", \"resolution\", \"batterycapacity\", \"ramsize\", \"storagesize\", \"highestcameraresolution\", \"launchprice(usd)\"], \"eval_pipeline\": {\"releasedate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"processorbrand\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"cpumanufacturingprocess\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"cpucorecount\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"batterycapacity\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"ramsize\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"storagesize\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"highestcameraresolution\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"launchprice(usd)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"screensize\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"resolution\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"modelname\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\nDo not need to be entirely strictly consistent\"}, \"cpumodel\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\njust provide the core part, 'for Samsung' is optional.\\nResolution：Reguard 2340x1080 and 1080x2340 both with equal and correct\"}, \"pre-installedos\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
 {"instance_id":"ws_en_005","query":"I’m planning to apply for full-time Master’s programs in civil engineering starting in 2026. Could you help me find the minimum GPA requirements for admission to Ivy League institutions in the US and Group of Eight universities in Australia?\n\nPlease organize the results in one Markdown table with the following columns: Country, University, Alliance, and Minimum GPA Requirement.\n\nNote: \nThe value of “Alliance” can only be “Ivy League” or “Group of Eight”\nIf no specified minimum GPA cutoff in official resources, list as \"no minimum GPA specified\"\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"university\"], \"required\": [\"country\", \"university\", \"alliance\", \"minimumgparequirement\"], \"eval_pipeline\": {\"country\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"university\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"alliance\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"minimumgparequirement\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\nSupplementary description other than  minimum scores are optional.\"}}}","language":"en"}
-{"instance_id":"ws_en_006","query":"Could you list every single concert on Taylor Swift’s tour from January 1, 2010, to May 1, 2025, including the specific date, the concert’s English name, the country, the city, and the venue. Each show should be on its own line, in chronological order from earliest to latest.\n\nPlease organize the results in one Markdown table with the following columns:\nDate, the Concert’s English Name, Host Country, Host City, Host Venue\n\nNotes: Do not use date ranges for Date, list it in the format of “Day Month, Year”, for example: 4th June, 2011\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"date\"], \"required\": [\"date\", \"theconcert’senglishname\", \"hostcountry\", \"hostcity\", \"hostvenue\"], \"eval_pipeline\": {\"date\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"theconcert’senglishname\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"hostcountry\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"hostcity\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"hostvenue\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
-{"instance_id":"ws_en_007","query":"I would like a broad overview of multiple spaceflight missions across different NASA programs, including Mercury, Gemini, Apollo, and Skylab. Please provide the mission name,  mission type, crew (astronaut) names (separated by comma), backup crew names (separated by comma), launch date (accurate to the day), launch pad, landing date (accurate to the day), landing location,  vehicle, orbit duration, and mission status (success or fail). \n\nPlease organize the results in one Markdown table, the column names should be in the following order:  Mission Name, Mission Type, Crew Names, Backup Crew Names, Launch Date, Launch Pad, Landing Date, Landing Location, Vehicle, Orbit Duration, Mission Status.\nLaunch date and landing date should be in the yyyy-mm-dd format, such as 1999-01-01. If you cannot find the information, fill in \/.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"launchdate\"], \"required\": [\"missionname\", \"missiontype\", \"crewnames\", \"backupcrewnames\", \"launchdate\", \"launchpad\", \"landingdate\", \"landinglocation\", \"vehicle\", \"orbitduration\", \"missionstatus\"], \"eval_pipeline\": {\"landingdate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"launchdate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"missionname\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"missionstatus\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"missiontype\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\n Different reference information sources can be accepted. For example, the reference answer is taken from the NASA official website (Human Spaceflight), and the model's response is taken from wiki (rendezvous). For the same flight mission, both can be considered correct.\"}, \"crewnames\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"backupcrewnames\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"launchpad\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\n The same launch pad may have different names at different times. As long as they refer to the same launch pad, it can be considered correct\"}, \"landinglocation\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"vehicle\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"orbitduration\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
-{"instance_id":"ws_en_008","query":"Could I obtain monthly data from January 2025 to May 2025 (including January 2025 and May 2025) for NASDAQ, NYSE, Shanghai Stock Exchange, Shenzhen Stock Exchange, and Hong Kong Exchanges and Clearing?\n\n Please organize the results in one Markdown table with the following column names in order: Exchange Name, Statistical Month, Total Trading Value (USD millions), Total Number of Listed Companies, Domestic Market Capitalization (USD millions), Index Levels\nFor the statistical month, keep the format as January 2025. \nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```","evaluation":"{\"unique_columns\": [\"exchangename\", \"statisticalmonth\"], \"required\": [\"exchangename\", \"statisticalmonth\", \"totaltradingvalue(usdmillions)\", \"totalnumberoflistedcompanies\", \"domesticmarketcapitalization(usdmillions)\", \"indexlevels\"], \"eval_pipeline\": {\"totaltradingvalue(usdmillions)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"totalnumberoflistedcompanies\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"domesticmarketcapitalization(usdmillions)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"indexlevels\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"exchangename\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"statisticalmonth\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"en"}
+{"instance_id":"ws_en_006","query":"Could you list every single concert on Taylor Swift’s official tour from January 1, 2010, to May 1, 2025, including the specific date, the concert’s English name, the country, the city, and the venue. Each show should be on its own line, in chronological order from earliest to latest.\n\nPlease organize the results in one Markdown table with the following columns:\nDate, the Concert’s English Name, Host Country, Host City, Host Venue\n\nNotes: Do not use date ranges for Date, list it in the format of “Day Month, Year”, for example: 4th June, 2011\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"date\"], \"required\": [\"date\", \"theconcert’senglishname\", \"hostcountry\", \"hostcity\", \"hostvenue\"], \"eval_pipeline\": {\"date\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"theconcert’senglishname\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"hostcountry\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"hostcity\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"hostvenue\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
+{"instance_id":"ws_en_007","query":"I would like a broad overview of multiple spaceflight missions across different NASA programs, including Mercury, Gemini, Apollo, and Skylab. Please provide the mission name,  mission type, crew (astronaut) names (separated by comma), backup crew names (separated by comma), launch date (accurate to the day), launch pad, landing date (accurate to the day), landing location,  vehicle, orbit duration, and mission status (success or fail). Exclude test project in your answer.\n\nPlease organize the results in one Markdown table, the column names should be in the following order:  Mission Name, Mission Type, Crew Names, Backup Crew Names, Launch Date, Launch Pad, Landing Date, Landing Location, Vehicle, Orbit Duration, Mission Status.\nLaunch date and landing date should be in the yyyy-mm-dd format, such as 1999-01-01. If you cannot find the information, fill in \/.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"launchdate\"], \"required\": [\"missionname\", \"missiontype\", \"crewnames\", \"backupcrewnames\", \"launchdate\", \"launchpad\", \"landingdate\", \"landinglocation\", \"vehicle\", \"orbitduration\", \"missionstatus\"], \"eval_pipeline\": {\"landingdate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"launchdate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"missionname\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"missionstatus\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"missiontype\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\n Different reference information sources can be accepted. For example, the reference answer is taken from the NASA official website (Human Spaceflight), and the model's response is taken from wiki (rendezvous). For the same flight mission, both can be considered correct.\"}, \"crewnames\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"backupcrewnames\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"launchpad\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\n The same launch pad may have different names at different times. As long as they refer to the same launch pad, it can be considered correct\"}, \"landinglocation\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"vehicle\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"orbitduration\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
+{"instance_id":"ws_en_008","query":"Could I obtain monthly data from January 2025 to May 2025 (including January 2025 and May 2025) for NASDAQ, NYSE, Shanghai Stock Exchange, Shenzhen Stock Exchange, and Hong Kong Exchanges and Clearing?\n\n Please organize the results in one Markdown table with the following column names in order: Exchange Name, Statistical Month, Total Trading Value (USD millions), Total Number of Listed Companies, Domestic Market Capitalization (USD millions), Index Levels\nFor the statistical month, keep the format as January 2025. \nFor total trading value and domestic market capitalization, both of the terms refer to equity.\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```","evaluation":"{\"unique_columns\": [\"exchangename\", \"statisticalmonth\"], \"required\": [\"exchangename\", \"statisticalmonth\", \"totaltradingvalue(usdmillions)\", \"totalnumberoflistedcompanies\", \"domesticmarketcapitalization(usdmillions)\", \"indexlevels\"], \"eval_pipeline\": {\"totaltradingvalue(usdmillions)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"totalnumberoflistedcompanies\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"domesticmarketcapitalization(usdmillions)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"indexlevels\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"exchangename\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"statisticalmonth\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"en"}
 {"instance_id":"ws_en_009","query":"I want to apply for a full-time MA program at Goldsmiths College, University of London. I am interested in the department of Media, Communications and Cultural Studies. Could you help me find all the degrees available in this department for 2025 entry?\n\nPlease organize the results in one Markdown table with the following columns:\nProgram Name, Length, Annual International Tuition Fees, IELTS Score Requirement, Compulsory Module Titles, Credits. \nNote:\nThe meaning of 'length' is the duration of this master's program.\nFor any missing information, fill in '\/'.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"programname\"], \"required\": [\"programname\", \"length\", \"annualinternationaltutionfees\", \"ieltsscorerequirement\", \"compulsorymoduletitles\", \"credits\"], \"eval_pipeline\": {\"programname\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"annualinternationaltutionfees\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"compulsorymoduletitles\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\n It is acceptable if the corresponding scores are missing\"}, \"credits\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\n It is acceptable if the model outputs the calculated score. For example, it is acceptable that the model outputs \\\"60\\\" for \\\"30 + 30\\\".\"}, \"length\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"ieltsscorerequirement\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
 {"instance_id":"ws_en_010","query":"Could you provide an updated list of all officially recognized Areas of Outstanding Natural Beauty (AONBs) and National Scenic Area (NSA) in the UK as of 2024? Please include the following: name of each scenic area, designation category (AONB or NSA), regions (select from England, Scotland, Wales, and Northern Ireland), designation time (the year it was designated as an AONB\/NSA for the first time), responsible public body (select from Natural England, Natural Resources Wales, Northern Ireland Environment Agency, or NatureScot), counties (list all administrative counties it covers, separated by commas), and the official website.\n\nPlease organize the results in one Markdown table with the following columns:\nDesignation Category, Area Name, Regions, Designation Time, Responsible Public Body, Counties, Website\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"areaname\"], \"required\": [\"designationcategory\", \"areaname\", \"regions\", \"designationtime\", \"responsiblepublicbody\", \"counties\", \"website\"], \"eval_pipeline\": {\"designationcategory\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"designationtime\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"areaname\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"regions\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"website\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\n The website links may vary, as long as the main body remains the same.\"}, \"counties\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"responsiblepublicbody\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
 {"instance_id":"ws_en_011","query":"According to the J.D. Power Awards Best Car Ratings of 2025, please compile the information on the 2025 award winners for highest dependability in compact vehicles, including both the award types (Compact Car, Compact Premium Car, Compact Premium SUV, or Compact SUV) and the corresponding vehicle details.\n\nPlease organize the results in one Markdown table with the following columns:\nAward Type, Vehicle Make, Year, Model, Major Trims, Body style, Fuel type(s), Drivetrain(s), Passengers, Doors, Overall Ratings 2025, Quality & Reliability Score 2025, Driving Experience Score 2025, Resale Value Score 2025.\nIf the awarded model is categorized as a series (rather than a single specific model), please list all the major trims within that series, separated by commas.\nSeparate multiple fuel types by comma.\nOnly use Arabic numerals for the columns Year, Passengers, Doors, Overall Ratings 2025, Quality & Reliability Score 2025, Driving Experience Score 2025, Resale Value Score 2025.\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```","evaluation":"{\"unique_columns\": [\"awardtype\"], \"required\": [\"awardtype\", \"vehiclemake\", \"year\", \"model\", \"majortrims\", \"bodystyle\", \"fueltype(s)\", \"drivetrain(s)\", \"passengers\", \"doors\", \"overallratings2025\", \"quality&reliabilityscore2025\", \"drivingexperiencescore2025\", \"resalevaluescore2025\"], \"eval_pipeline\": {\"year\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"passengers\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"doors\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"overallratings2025\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"quality&reliabilityscore2025\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"drivingexperiencescore2025\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"resalevaluescore2025\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"vehiclemake\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"model\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"majortrims\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"bodystyle\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"fueltype(s)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\nWhen \\\"hybrid\\\" is included in the reference answer(for example, \\\"plug-inhybrid\\\"), it is acceptable as long as \\\"hybrid\\\" is present in the response.\"}, \"drivetrain(s)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"awardtype\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"en"}
@@ -14,18 +14,18 @@
 {"instance_id":"ws_en_014","query":"What are the world's busiest airports by passenger traffic? List the top ten busiest airports between 2020-2024 (including 2020 and 2024). I want to know about their rank, specific location (including city, district, road), total passengers (with precision to the person) and code(IATA\/ICAO). \n\n Please organize the results in one Markdown table with the following column names in order: \nYear, Airport, Rank, Location, Total Passengers, Code(IATA\/ICAO) \n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"year\", \"rank\"], \"required\": [\"year\", \"airport\", \"rank\", \"location\", \"totalpassengers\", \"code(iata\/icao)\"], \"eval_pipeline\": {\"year\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"rank\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"code(iata\/icao)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"totalpassengers\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"location\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"airport\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
 {"instance_id":"ws_en_015","query":"I would like to manage my property and am interested in learning about the top 50 property managers published by the National Multifamily Housing Council for 2025, including their ranking, company name, number of units managed in 2024, and the year the company was founded.\n\nPlease output the organized data in the format of one Markdown table.\nThe column names in the table are as follows in sequence:\nRank, Company Name, Number of Managed Units in 2024, Company Establishment Time.\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"rank\"], \"required\": [\"rank\", \"companyname\", \"numberofmanagedunitsin2024\", \"companyestablishmenttime\"], \"eval_pipeline\": {\"rank\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"companyestablishmenttime\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"numberofmanagedunitsin2024\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"companyname\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
 {"instance_id":"ws_en_016","query":"Michael Phelps is one of the most decorated Olympians in history. \nI am interested in compiling his medal achievements of individual events from major international competitions, specifically the Olympic Games, and the World Aquatics Championships.\n\nPlease provide this information covering the period from 2000 to 2016 (including 2000 and 2016) for the competitions in which he has won gold, silver, and bronze medals, point out his standing in the competition as well as the specific individual's name who received each of those medals. Please also include the date (mm-dd-yy) and location of the final , the name of the swimming meet and event, and the record time of Michael Phelps.\n\nPlease organize the results in one Markdown table with the following columns:\nDate, Location, Meet, Event, Time, Standing, Gold, Silver, Bronze\nNotes:\nPlease include the city that held the final competition under Location, in the format of \"City, country\", for example: Beijing, China. \nIf multiple people get the same ranking, please separate their names with commas. In the Standing column, select 1, 2 or 3 according to the information you have found.\nTime is accurate to 1\/100 second. Output format example: 1:23.01\nWrite the full name or abbreviation of the event, for example, 200m butterfly or 200m fly.\nIf there is content that cannot be found, use \"-\" instead.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```","evaluation":"{\"unique_columns\": [\"time\"], \"required\": [\"date\", \"location\", \"meet\", \"event\", \"time\", \"standing\", \"gold\", \"silver\", \"bronze\"], \"eval_pipeline\": {\"location\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"time\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"standing\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"meet\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"event\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"gold\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"silver\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"bronze\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"date\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\n The date can fluctuate by one day before or after.\"}}}","language":"en"}
-{"instance_id":"ws_en_017","query":"Please help me organize a table based on The 13 USA National Parks given below. I need the following information: the name of the national park, the states it is located in, total visitor spending in 2023, the number of visitors in 2023, the serving superintendent as of May 2025, Mailing Address, and hunting Regulations. \n1. Pearl Harbor National Memorial  \n2. Muir Woods National Monument  \n3. Mount Rushmore National Memorial  \n4. Arches National Park  \n5. White Sands National Park  \n6. Zion National Park  \n7. Yellowstone National Park  \n8. Acadia National Park  \n9. Crater Lake National Park  \n10. Haleakala National Park  \n11. Great Smoky Mountains National Park  \n12. Redwood National and State Parks  \n13. Canyonlands National Park  \n\nPlease output the organized data in one Markdown table format. The column names in the table should be: National Park Name, State(s), 2023 Total Visitor Spending(million dollars), 2023 Visitors Number(million), Superintendent, Mailing Address, Hunting Regulations.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"nationalparkname\"], \"required\": [\"nationalparkname\", \"state(s)\", \"2023totalvisitorspending(milliondollars)\", \"2023visitorsnumber(million)\", \"superintendent\", \"mailingaddress\", \"huntingregulations\"], \"eval_pipeline\": {\"2023totalvisitorspending(milliondollars)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2023visitorsnumber(million)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"state(s)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"superintendent\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"mailingaddress\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"huntingregulations\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"nationalparkname\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"en"}
+{"instance_id":"ws_en_017","query":"Please help me organize a table based on The 13 USA National Parks given below. I need the following information: the name of the national park, the states it is located in, total visitor spending in 2023, the number of visitors in 2023, the serving superintendent as of May 2025, Mailing Address, and hunting Regulations. \n1. Pearl Harbor National Memorial  \n2. Muir Woods National Monument  \n3. Mount Rushmore National Memorial  \n4. Arches National Park  \n5. White Sands National Park  \n6. Zion National Park  \n7. Yellowstone National Park  \n8. Acadia National Park  \n9. Crater Lake National Park  \n10. Haleakala National Park  \n11. Great Smoky Mountains National Park  \n12. Redwood National and State Parks  \n13. Canyonlands National Park  \n\nPlease output the organized data in one Markdown table format. The column names in the table should be: National Park Name, State(s), 2023 Total Visitor Spending(million dollars), 2023 Visitors Number(million), Superintendent, Mailing Address, Hunting Regulations.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"nationalparkname\"], \"required\": [\"nationalparkname\", \"state(s)\", \"2023totalvisitorspending(milliondollars)\", \"2023visitorsnumber(million)\", \"superintendent\", \"mailingaddress\", \"huntingregulations\"], \"eval_pipeline\": {\"2023totalvisitorspending(milliondollars)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2023visitorsnumber(million)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"state(s)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"superintendent\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"mailingaddress\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"huntingregulations\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\njust focus on the overall regulation if hunting is prohibited or allowed.\"}, \"nationalparkname\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"en"}
 {"instance_id":"ws_en_018","query":"Need to analyze the trend of the U.S. federal government spending and deficit before and after the pandemic. Please provide me with the following data through fiscal years 2015-2024: the federal Budget (trillion), the federal spending (trillion), the federal deficit (trillion), the national debt (trillion), and the net interest cost on the gross federal debt (trillion).\n\nPlease organize the results in one Markdown table with the following column names in order: \nFiscal Year, Federal Budget, Federal Spending, Federal Deficit, National Debt, Net Interest Cost.\n \nUnder the Fiscal Year, state the statistics like FY2015, FY2016.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```","evaluation":"{\"unique_columns\": [\"fiscalyear\"], \"required\": [\"fiscalyear\", \"federalbudget\", \"federalspending\", \"federaldeficit\", \"nationaldebt\", \"netinterestcost\"], \"eval_pipeline\": {\"fiscalyear\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"federalbudget\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"federalspending\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"federaldeficit\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"nationaldebt\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"netinterestcost\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}}}","language":"en"}
 {"instance_id":"ws_en_019","query":"I am working on a tracking report regarding the biotechnology and pharmaceutical companies that went public on NASDAQ in 2024, namely: CG Oncology, Zenas BioPharma, Upstream Bio, MBX Biosciences, and Metagenomi. I want to start by organizing some data. Please help me find out the listing date (as in yyyy\/mm\/dd), listing board(full name), initial offering price, total funds raised in 2024. Additionally, I need the revenue, net income attributable to shareholders and R&D expenses disclosed in their 2024 annual reports for these companies. All amounts should be in United States dollars, numerical only, and retained to two decimals. If no relevant data can be found, please fill in with N\/A.\n\nPlease output the organized data in the format of one Markdown table.\nThe column names in the table are as follows in sequence:\nCompany Name, Listing board, Bloomberg ticker, Listing Date, Initial Offering Price(per share), Total Funds Raised in 2024(million), Revenue in 2024(million), Net Income Attributable to Shareholders(million), R&D Expenses(million)\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"companyname\"], \"required\": [\"companyname\", \"listingboard\", \"bloombergticker\", \"listingdate\", \"initialofferingprice(pershare)\", \"totalfundsraisedin2024(million)\", \"revenuein2024(million)\", \"netincomeattributabletoshareholders(million)\", \"r&dexpenses(million)\"], \"eval_pipeline\": {\"companyname\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"listingboard\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"listingdate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"initialofferingprice(pershare)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"revenuein2024(million)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"netincomeattributabletoshareholders(million)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"r&dexpenses(million)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"totalfundsraisedin2024(million)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"bloombergticker\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
 {"instance_id":"ws_en_020","query":"Please provide a complete list, up to 2024 (excluding 2024), of UNESCO World Heritage sites located in North America (namely US, Canada, and Mexico) , including their location (the main county\/city where the sites are located in), type (cultural, natural, or mixed), area (in hectares), and the year of their recognition.  For any information not found online, please fill in \"NA\".\n\nPlease output the organized data in a single Markdown table. The column names in the table should be as follows, in order:\nCountry, Name of the Heritage Site, Type of Heritage, Location, Area, Year of Recognition\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```","evaluation":"{\"unique_columns\": [\"nameoftheheritagesite\"], \"required\": [\"country\", \"nameoftheheritagesite\", \"typeofheritage\", \"location\", \"area\", \"yearofrecognition\"], \"eval_pipeline\": {\"yearofrecognition\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"area\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"country\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"typeofheritage\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"location\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\nIf multiple addresses are included, they all need to correspond. For a specific location, it is correct as long as the main city\/county of the location is provided, only province is not enough.\"}, \"nameoftheheritagesite\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
 {"instance_id":"ws_en_021","query":"Could you provide a detailed list of Michelin three-star restaurants in Paris, France as of December 31, 2024? I especially want to know the name, main cuisine style and exact address of each restaurant.\n\nPlease organize the results in one Markdown table with the following columns:\nRestaurant, Main Cuisine Style, Address\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```","evaluation":"{\"unique_columns\": [\"restaurant\"], \"required\": [\"restaurant\", \"maincuisinestyle\", \"address\"], \"eval_pipeline\": {\"restaurant\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"maincuisinestyle\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\nOne-to-one matching is unnecessary. It suffices as long as the content is relevent.\"}, \"address\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
-{"instance_id":"ws_en_022","query":"I need a list of the top 10 most-streamed songs globally and top 10 most-streamed songs in the U.S. on Spotify in 2024. The header names should include Category, Song Rank, Song Title, Singer, Language, Songwriter(s), Producer(s), and Release Date.\n\nPlease organize the results in one Markdown table with the following columns:\nCategory,Rank, Title,Singer,Language,Songwriter(s),Producer(s),Release Date.\nNote:\n1.The song rankings need to be sorted in descending order.\n2. Most-streamed songs globally and U.S. most-streamed songs need to be distinguished in the \"Category\" column.\n3. Release date should be written as YYYY\/MM\/DD\n4. If there is more than one songwriter or producer, separate them by comma.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"category\", \"rank\"], \"required\": [\"category\", \"rank\", \"title\", \"singer\", \"language\", \"songwriter(s)\", \"producer(s)\", \"releasedate\"], \"eval_pipeline\": {\"category\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"rank\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"language\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"releasedate\": {\"preprocess\": [\"norm_date\"], \"metric\": [\"date_near\"]}, \"singer\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"songwriter(s)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\n It suffices to match one or more from reference answer, and no one outside of reference answer is allowed\"}, \"producer(s)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\n It suffices to match one or more from reference answer, and no one outside of reference answer is allowed\"}, \"title\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\n It is acceptable as long as the main body is consistent, with or without \\\"feat xx\\\" and \\\"with xx\\\".\"}}}","language":"en"}
+{"instance_id":"ws_en_022","query":"I need a list of the top 10 most-streamed songs globally and top 10 most-streamed songs in the U.S. on Spotify in 2024. The header names should include Category, Song Rank, Song Title, Singer, Language, Songwriter(s), Producer(s), and Release Date.\n\nPlease organize the results in one Markdown table with the following columns:\nCategory,Rank, Title,Singer,Language,Songwriter(s),Producer(s),Release Date.\nNote:\n1.The song rankings need to be sorted in descending order.\n2. Most-streamed songs globally and U.S. most-streamed songs need to be distinguished in the \"Category\" column.\n3. Release date should be written as YYYY\/MM\/DD\n4. If there is more than one songwriter or producer, separate them by comma.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"category\", \"rank\"], \"required\": [\"category\", \"rank\", \"title\", \"singer\", \"language\", \"songwriter(s)\", \"producer(s)\", \"releasedate\"], \"eval_pipeline\": {\"category\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"rank\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"language\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"releasedate\": {\"preprocess\": [\"norm_date\"], \"metric\": [\"date_near\"]}, \"singer\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"songwriter(s)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\n It suffices to match one or more from reference answer, and a few outside of reference answer is also allowed\"}, \"producer(s)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\n It suffices to match one or more from reference answer, and a few outside of reference answer is also allowed\"}, \"title\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\n It is acceptable as long as the main body is consistent, with or without \\\"feat xx\\\" and \\\"with xx\\\".\"}}}","language":"en"}
 {"instance_id":"ws_en_023","query":"Please help me find articles published in the American Journal of Cultural Sociology, the American Journal of Economic Sociology, and the American Journal of Sociology from 2020 to 2024 (including 2020 and 2024), with titles including \"migrate\", \"immigrate\", and their various variations. You need to collect the name of the journal, the issued date of the journal (as in the format of MM YYYY, i.e., June 2024), the title of the article, volume and issue number, page number(s), author(s).\n\nPlease organize the results in one Markdown table with the following columns:\nJournal, Issued Date, Article Title, Author(s), Volume Number, Issue Number, Page Number(s).\n\nNotes:\nRecord the full name of the author(s), and if there is more than one author, separate each name by semicolon.\nIf the issued date, volume number, issue number, or page number is not applicable, leave the cell blank.\nFor page number, format it as xxx-xxx.\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```","evaluation":"{\"unique_columns\": [\"journal\", \"articletitle\"], \"required\": [\"journal\", \"issueddate\", \"articletitle\", \"author(s)\", \"volumenumber\", \"issuenumber\", \"pagenumber(s)\"], \"eval_pipeline\": {\"volumenumber\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"issuenumber\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"journal\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"issueddate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"articletitle\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"author(s)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"pagenumber(s)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
-{"instance_id":"ws_en_024","query":"Please research the following information about Super Bowl: date, champions, host stadium, host city, Nielsen HHLD rating, average viewership across all platforms (million, round to one decimal place), and the half-time show sponsors.  I need annual statistics from 2001 to 2025 (including 2001 and 2025). If viewership is unavailable, use '-' instead.\n\nPlease give me the organized data in the format of one Markdown table, with the column names as: \nYear, Champion, Host Stadium, Host City,  Nielsen HHLD Rating, Viewership (million), Half-time Show Sponsors.\nFor host city, give the full name of the city, like Houston. \n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"year\"], \"required\": [\"year\", \"champion\", \"hoststadium\", \"hostcity\", \"nielsenhhldrating\", \"viewership(million)\", \"half-timeshowsponsors\"], \"eval_pipeline\": {\"champion\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"half-timeshowsponsors\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"hoststadium\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"hostcity\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"year\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"nielsenhhldrating\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"viewership(million)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}}}","language":"en"}
+{"instance_id":"ws_en_024","query":"Please research the following information about Super Bowl: date, champions, host stadium, host city, Nielsen HHLD rating, average viewership across all platforms (million, round to one decimal place), and the half-time show sponsors.  I need annual statistics from 2001 to 2025 (including 2001 and 2025). If viewership is unavailable, use '-' instead.\n\nPlease give me the organized data in the format of one Markdown table, with the column names as: \nYear, Champion, Host Stadium, Host City,  Nielsen HHLD Rating, Viewership (million), Half-time Show Sponsors.\nFor host city, give the full name of the city, like Houston. \n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"year\"], \"required\": [\"year\", \"champion\", \"hoststadium\", \"hostcity\", \"nielsenhhldrating\", \"viewership(million)\", \"half-timeshowsponsors\"], \"eval_pipeline\": {\"champion\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"hoststadium\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"hostcity\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"year\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"nielsenhhldrating\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"viewership(million)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"half-timeshowsponsors\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
 {"instance_id":"ws_en_025","query":"Make a comprehensive, verified list of abortion clinics currently operating in California (2024) within 65 miles of 1417-1499 E Fedora Ave, Fresno, California, 93704. Omit any facilities that closed during 2024.\n\nPlease organize the results in one Markdown table with the following column names in order:\nClinic, County, Full Address, Phone Number, Saturday Operation Hours\nList the phone number in the format of (xxx) xxx-xxxx  \nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```","evaluation":"{\"unique_columns\": [\"clinic\"], \"required\": [\"clinic\", \"county\", \"fulladdress\", \"phonenumber\", \"saturdayoperationhours\"], \"eval_pipeline\": {\"county\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"phonenumber\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"fulladdress\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"saturdayoperationhours\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"clinic\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"en"}
 {"instance_id":"ws_en_026","query":"Help me collect the basic electoral information of 50 U.S. states in 2024. 1) the number of congressional districts; 2) number of electors; 3) the name of the governor in 2024 and his\/her party affiliation, like \"Gavin Newsom (D)\"; 4) the names of the two senators in 2024 and their party affiliations separately.\n\nPlease present the information in one Markdown table with these column headers: State, Number of Congressional Districts, Number of Electors, Governor (Party), Senator 1 (Party) & Senator 2 (Party) \n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.\n\nFor example, \n```markdown\n| State           | Number of Congressional Districts | Number of Electors | Governor (Party)     | Senator 1 (Party)& Senator 2 (Party) |\n|-----------------|-----------------------------------|--------------------|----------------------|-------------------|-------------------|\n| Alabama         | 7                                 | 9                  | Kay Ivey (R)         | Tommy Tuberville (R)& Katie Britt (R)   |","evaluation":"{\"unique_columns\": [\"state\"], \"required\": [\"state\", \"numberofcongressionaldistricts\", \"numberofelectors\", \"governor(party)\", \"senator1(party)&senator2(party)\"], \"eval_pipeline\": {\"numberofcongressionaldistricts\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"numberofelectors\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"governor(party)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"senator1(party)&senator2(party)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\n\\\"Joe Manchin (D)\\\" and  \\\"Joe Manchin (I)\\\" can be substituted for each other.\"}, \"state\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
 {"instance_id":"ws_en_027","query":"I want to travel to London soon and I bought a London Pass.  Please help me find out which museums the London Pass currently provides free access to as of 2025. I need the museum, last entry time on weekends,  normal ticket price for one adult, and its specific address. I have no interest in sports so just skip the relevant museums.\n\nPlease present the information in one Markdown table with these column headers: Museum, Last Entry Time, Ticket Price (Pounds), and Address. \nFormat the \"Last Entry Time\" like \"4:00 pm\".\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"museum\"], \"required\": [\"museum\", \"lastentrytime\", \"ticketprice(pounds)\", \"address\"], \"eval_pipeline\": {\"museum\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"ticketprice(pounds)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"lastentrytime\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"address\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
-{"instance_id":"ws_en_028","query":"I'm curious about the market shares of different North America burger brands. Could you provide me the total restaurants number of McDonald’s, Burger King, Wendy’s, Shake Shack, and KFC stores worldwide, and in New York, Detroit, and Seattle as of the end of 2024? Please obtain the information from relevant financial reports or official websites. If the information is not available, mark it as \"nan\".\n\nPlease present the organized data in one Markdown table format.\nThe  columns  names are as follows:\nBrand, Worldwide, New York, Detroit, Seattle\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"brand\"], \"required\": [\"brand\", \"worldwide\", \"newyork\", \"detroit\", \"seattle\"], \"eval_pipeline\": {\"brand\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"newyork\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"detroit\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"seattle\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"worldwide\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.0}}}","language":"en"}
+{"instance_id":"ws_en_028","query":"I'm curious about the market shares of different North America burger brands. Could you provide me the total restaurants number of McDonald’s, Burger King, Wendy’s, Shake Shack, and KFC stores worldwide, and in New York, Detroit, and Seattle as of the end of 2024? Please obtain the information from relevant financial reports. If the information is not available, mark it as \"nan\".\n\nPlease present the organized data in one Markdown table format.\nThe  columns  names are as follows:\nBrand, Worldwide, New York, Detroit, Seattle\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"brand\"], \"required\": [\"brand\", \"worldwide\", \"newyork\", \"detroit\", \"seattle\"], \"eval_pipeline\": {\"brand\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"newyork\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"detroit\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"seattle\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"worldwide\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.0}}}","language":"en"}
 {"instance_id":"ws_en_029","query":"I’m a huge Marvel fan, but I’ve never gone through the entire Marvel Cinematic Universe in order. Could you compile a complete list of MCU films released up to December 30, 2024? For each movie, please include the title, its U.S. release date (formatted as “Month Day, Year” — e.g., March 24, 2000), along with its domestic and worldwide box-office grosses.\n\n Please organize the results in one Markdown table with the specified column order:\nMovie, Release Date, US Box Office (dollar), Worldwide Box office(dollar)\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```","evaluation":"{\"unique_columns\": [\"movie\"], \"required\": [\"movie\", \"releasedate\", \"usboxoffice(dollar)\", \"worldwideboxoffice(dollar)\"], \"eval_pipeline\": {\"releasedate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"movie\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"usboxoffice(dollar)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"worldwideboxoffice(dollar)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}}}","language":"en"}
 {"instance_id":"ws_en_030","query":"Please provide a summary of recent decisions made by the U.S. Supreme Court from January 1, 2025, to May 31, 2025. The information should be presented in a tabular format with the following headings:Case Name, Case Date (YYYY-MM-DD), Docket Number, Opinion Author, Judgment Outcome (Just give me a quick conclusion, such as affirmed, reversed, vacated, injunction granted, remanded). Please skip short per curiam opinions and numerous unsigned summary orders made by the Supreme Court.\n\nPlease organize the results in one Markdown table with the following columns:\nCase Name, Case Date, Docket Number, Opinion Author, Judgment Outcome\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"casename\"], \"required\": [\"casename\", \"casedate\", \"docketnumber\", \"opinionauthor\", \"judgmentoutcome\"], \"eval_pipeline\": {\"casedate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"casename\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"docketnumber\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"opinionauthor\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"judgmentoutcome\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
 {"instance_id":"ws_en_031","query":"I'm putting together an introductory video on Angelina Jolie, Nicole Kidman, Cate Blanchett, and Jodie Foster.  Please find out every film in which each actress had a role between 2010 and 2020 (including 2010 and 2020).  For each movie, list the title, the character's name, the release year, and any awards they won.  Short genre projects and those for which they only provided voice acting should be excluded.\n\nPlease output the organized data in the format of one Markdown table.\nThe table's columns, in order, are:\nActress, Year of Release, Title of Film, Character Name, Awards.\nNotes: if the film didn't win any awards, please fill in NA.\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"titleoffilm\"], \"required\": [\"actress\", \"yearofrelease\", \"titleoffilm\", \"charactername\", \"awards\"], \"eval_pipeline\": {\"yearofrelease\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"titleoffilm\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"actress\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"charactername\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"awards\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
@@ -44,19 +44,19 @@
 {"instance_id":"ws_en_044","query":"Hey, I'm gonna be in South Korea for a whole month, and I need your help grabbing a list of all the museums in Jongno or Gangnam district in Seoul that are still open for regular business as of June 2025 and show up on Google Maps as well. For each spot, can you include stuff like: the address, when it's open (hours) except holidays, whether it's free to get in or if there's a charge for an adult on regular days?\n\nPresent the data in one Markdown table with columns: Museum, Address, Business Hours, Admission Fee Status (free or charged).\n\nNote: Address should be in the form No. road\/street, District, Seoul. For example, 37 Samcheong-ro, Jongno District, Seoul.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"museum\"], \"required\": [\"museum\", \"address\", \"businesshours\", \"admissionfeestatus(freeorcharged)\"], \"eval_pipeline\": {\"museum\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"address\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"businesshours\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\nIt is acceptable if closure dates are not mentioned.\"}, \"admissionfeestatus(freeorcharged)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
 {"instance_id":"ws_en_045","query":"According to the list of research libraries in 2023 released by IPEDS, please help me organize the information of the top 50 libraries, including the institution and library name, library ID, total volumes, and funding source. The possible values for \"Funding source\" are \"privately-funded\" and \"publicly-funded.\"\n\nPlease output the organized data in the format of one Markdown table.\nThe column names are as follows in sequence: Ranking, Library, Institution, Library ID, Total Volumes, Funding Source.\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```","evaluation":"{\"unique_columns\": [\"libraryid\"], \"required\": [\"ranking\", \"library\", \"institution\", \"libraryid\", \"totalvolumes\", \"fundingsource\"], \"eval_pipeline\": {\"libraryid\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"fundingsource\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"totalvolumes\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.0}, \"ranking\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"library\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"institution\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
 {"instance_id":"ws_en_046","query":"Compile comprehensive data for all major international tournaments during Lang Ping's tenure (2005-2008) as head coach of the U.S. Women's National Volleyball Team, including: Year, Event, Event Venue(precise to the country), roster of female competitors in their final match per event, and final position (e.g., Silver Medal, 4th Place). Major tournaments encompass the Olympic Games, FIVB Volleyball Women's World Championship, FIVB Volleyball Women's World Cup, FIVB Volleyball World Grand Prix, FIVB Volleyball Women's World Grand Champions Cup, and Montreux Volley Masters. You may separate the competitors' names by comma.\n\nPlease organize the results in one Markdown table with the following columns:\nYear\nEvent\nEvent Venue\nRoster of Female Competitors\nFinal Position\n\nUse '-' if no relevant information is available.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"year\", \"event\"], \"required\": [\"year\", \"event\", \"eventvenue\", \"rosteroffemalecompetitors\", \"finalposition\"], \"eval_pipeline\": {\"year\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"eventvenue\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"finalposition\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"event\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"rosteroffemalecompetitors\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
-{"instance_id":"ws_en_047","query":"I am studying how partisan standoffs influence administrative efficiency and the macro-economy, so I need a quantitative overview of every federal government shutdown.\n\nPlease output one Markdown table with the columns, in this order:\n Start Date | End Date | Duration (days) | President |  Speaker of the House | Senate Majority Leader | Furloughed Employees | Estimated Loss (USD million) | Main Disputed Provisions |\n\nRequirements:\n1. Cover every officially recorded federal government shutdown between October 1976 and December 2024 (including October 1976 and December 2024).\n2. Date should be formatted as YYYY-MM-DD.\n3. Record the President, House and Senate leaders during the shutdown period.\n4. For furloughed employees, give the exact number. If there is no exact number or there is no employee furloughed, fill in with \"-\".\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"startdate\"], \"required\": [\"startdate\", \"enddate\", \"duration(days)\", \"president\", \"speakerofthehouse\", \"senatemajorityleader\", \"furloughedemployees\", \"estimatedloss(usdmillion)\", \"maindisputedprovisions\"], \"eval_pipeline\": {\"startdate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"enddate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"furloughedemployees\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"estimatedloss(usdmillion)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"president\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"speakerofthehouse\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"senatemajorityleader\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"maindisputedprovisions\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"duration(days)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\nan a difference of 1 day or less is cceptable.\"}}}","language":"en"}
+{"instance_id":"ws_en_047","query":"I am studying how partisan standoffs influence administrative efficiency and the macro-economy, so I need a quantitative overview of every federal government shutdown.\n\nPlease output one Markdown table with the columns, in this order:\n Start Date | End Date | Duration (days) | President |  Speaker of the House | Senate Majority Leader | Furloughed Employees | Estimated Loss (USD million) | Main Disputed Provisions |\n\nRequirements:\n1. Cover every officially recorded federal government shutdown between October 1976 and December 2024 (including October 1976 and December 2024).\n2. Date should be formatted as YYYY-MM-DD.\n3. Record the President, House and Senate leaders during the shutdown period.\n4. For furloughed employees, give the exact number. If there is no exact number or there is no employee furloughed, fill in with \"-\".\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"startdate\"], \"required\": [\"startdate\", \"enddate\", \"duration(days)\", \"president\", \"speakerofthehouse\", \"senatemajorityleader\", \"furloughedemployees\", \"estimatedloss(usdmillion)\", \"maindisputedprovisions\"], \"eval_pipeline\": {\"startdate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"enddate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"furloughedemployees\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"estimatedloss(usdmillion)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"president\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"speakerofthehouse\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"senatemajorityleader\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"maindisputedprovisions\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"duration(days)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\nan a difference of 1 day or less is acceptable.\"}}}","language":"en"}
 {"instance_id":"ws_en_048","query":"What's the correlation between police funds and violent-crime trends?  Using the 10 most populous U.S. cities from the 2020 Census, list each city’s FY 2024 police-department budget (USD bn, keep one digit). Include sworn police officer headcounts in 2024, the 2023 violent-crime rate (per 100k residents), the 2023 Homicides (per 100k residents) and the 2024 mayor and his\/her party. If a city uses a calendar-year budget, treat the 2024 calendar budget as FY 2024. Fill missing values with NA.\nThe crime rates and homicides should be rounded up to the nearest integer.\n\nPlease output one Markdown table with the columns in this order:\n City | FY 2024 Police Budget (USD bn) | Sworn Officers | 2023 Violent-Crime Rate | 2023 Homicides | 2024 Mayor | Party | Population (2020 Census)\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"city\"], \"required\": [\"city\", \"fy2024policebudget(usdbn)\", \"swornofficers\", \"2023violent-crimerate\", \"2023homicides\", \"2024mayor\", \"party\", \"population(2020census)\"], \"eval_pipeline\": {\"city\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"fy2024policebudget(usdbn)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"swornofficers\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"2023violent-crimerate\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"2023homicides\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"population(2020census)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"party\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"2024mayor\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
 {"instance_id":"ws_en_049","query":"I'd like to know the information about all Ford vehicles first launched and revived from January 1, 2010 to December 31, 2024 in the US (not including model updates or facelifts). Information is needed as follows: Model Name (Year), Price, Dimensions (L\/W\/H, mm), Wheelbase (mm), Maximum Torque (N·m), Front Suspension Type, Rear Suspension Type, Advanced Driving Assistance System (Please list all ADAS equipped in this model), Intelligent Parking Assist (Please list all IPA equipped in this model). All information is for the standard edition, and if it doesn't have ADAS or IPA, fill in '-'. \n\nPlease organize the results in one Markdown table with the following columns: Model Name, Price (USD, launch MSRPs), Dimensions L\/W\/H (mm), Wheelbase (mm), Max Torque (N·m), Front Suspension, Rear Suspension, Advanced-Driver-Assist Systems (ADAS²), Intelligent Parking Assist\nDimensions should adopt the data that exclude mirror and the launch MSRPs usually refers to price without destination fee.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"modelname\"], \"required\": [\"modelname\", \"price(launchmsrpsinusd)\", \"dimensionsl\/w\/h(mm)\", \"wheelbase(mm)\", \"maxtorque(n·m)\", \"frontsuspension\", \"rearsuspension\", \"advanced-driver-assistsystems(adas²)\", \"intelligentparkingassist\"], \"eval_pipeline\": {\"maxtorque(n·m)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"frontsuspension\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"rearsuspension\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"advanced-driver-assistsystems(adas²)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"intelligentparkingassist\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"price(launchmsrpsinusd)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\nResponses must correspond to answer one by one.\\nDimensions L\/W\/H (mm)：Responses must correspond to answer one by one. It is sufficient if the numerical values are approximately equal.\\nWheelbase (mm)：Responses must correspond to answer one by one.\\nAdvanced-Driver-Assist Systems (ADAS²)：Responses must correspond to answer one by one. Response mult list all ADAS equipped.\\nFront Suspension：Responses must correspond to answer one by one.\\nRear Suspension：Responses must correspond to answer one by one.\"}, \"dimensionsl\/w\/h(mm)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"wheelbase(mm)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"modelname\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"en"}
-{"instance_id":"ws_en_050","query":"For a comparative politics study on descriptive representation, compile the share of women in the lower or single chamber of every OECD member’s national parliament at four benchmark dates: 31 Dec 1995, 31 Dec 2005, 31 Dec 2015, and 31 Dec 2024.\n Exclude upper chambers. If a country joined the OECD after 1995, record NA for years before membership. As for the total seats, give the total seats of the lower parliament.\n\nPlease output one Markdown table with the columns, in this exact order:\n Country | Share of Women Seats 1995(%) | Total Seats 1995 | Share of Women Seats 2005(%) | Total Seats 2005 | Share of Women Seats 2015(%) | Total Seats 2015 |  Share of Women Seats 2024(%) | Total Seats 2024 \n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"country\"], \"required\": [\"country\", \"shareofwomenseats1995(%)\", \"totalseats1995\", \"shareofwomenseats2005(%)\", \"totalseats2005\", \"shareofwomenseats2015(%)\", \"totalseats2015\", \"shareofwomenseats2024(%)\", \"totalseats2024\"], \"eval_pipeline\": {\"shareofwomenseats1995(%)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"totalseats1995\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"shareofwomenseats2005(%)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"totalseats2005\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"shareofwomenseats2015(%)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"totalseats2015\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"shareofwomenseats2024(%)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"totalseats2024\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"country\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
-{"instance_id":"ws_en_051","query":"I’m interested in physics. Could you please provide a list of the Best 50 U.S. STEM High Schools 2024 according to 2024 U.S. News & World Report, including each school’s location (state), its STEM High Schools ranking, National Rankings position, State Rankings position, the grades it serves, total number of AP courses offered as reported for 2024?\nPlease output the organized data in one Markdown table format.\nThe column names should be in order as follows:                                                                                                                                                                  \nHigh School, States, STEM High Schools ranking, National Rankings, State Rankings, Served Grades, AP Courses.\nInstructions:\n1. Use only Arabic numerals for all rankings.  \n2. Write out the full state name—no abbreviations are permitted.  \n3. If any information is missing, enter \"NA.\"  \n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"highschool\"], \"required\": [\"highschool\", \"states\", \"stemhighschoolsranking\", \"nationalrankings\", \"staterankings\", \"servedgrades\", \"apcourses\"], \"eval_pipeline\": {\"stemhighschoolsranking\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"nationalrankings\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"apcourses\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"highschool\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"states\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"servedgrades\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"staterankings\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
-{"instance_id":"ws_en_052","query":"For an infrastructure-finance paper, I need to benchmark capital intensity of large offshore wind assets. List every offshore wind farm in European waters that was fully commissioned from 2010-01-01 to 2024-12-31 and whose nameplate capacity is ≥ 300 MW. Ignore projects still under construction or phases that are only partially energized by 2024.\n\n\nPlease output one Markdown table with the columns, in this exact order:\n Wind Farm | Sea \/ Basin | Capacity (MW) | Turbines Number | Turbine Model | Commissioning Year | Owner \/ Operator\nFill missing fields with “NA”.\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```","evaluation":"{\"unique_columns\": [\"windfarm\"], \"required\": [\"windfarm\", \"sea\/basin\", \"capacity(mw)\", \"turbinesnumber\", \"turbinemodel\", \"commissioningyear\", \"owner\/operator\"], \"eval_pipeline\": {\"commissioningyear\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"turbinesnumber\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"capacity(mw)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"windfarm\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"sea\/basin\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"turbinemodel\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\nthe level of detail in the model description may differ from that in the reference answer, as long as it can be inferred to refer to the same model.\"}, \"owner\/operator\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\nthe company mentioned in the answer to be evaluated is a subset of that mentioned in the reference answer\"}}}","language":"en"}
-{"instance_id":"ws_en_053","query":"I am preparing a policy brief on post-millennial commercial nuclear deployment and need a consolidated snapshot of every civil nuclear power reactor worldwide that entered commercial operation between 1 Jan 2008 and 31 Dec 2024.\n To keep the sample coherent, please exclude: research or prototype reactors, small modular units ≤ 300 MW(e) net capacity, and reactors that were permanently shut down before reaching commercial service.\n For each qualifying reactor, compile the following eight factual attributes—these data points are typically scattered across different industry reports (e.g., IAEA PRIS, WNA country profiles, corporate annual reports) and cannot all be gathered from a single page:\n1) Reactor Name (use the operator’s official English designation)  \n2)Country \n3) Reactor Type (e.g., PWR, BWR, VVER-1200, APR-1400, etc.) \n4) Net Electrical Capacity (MW) — nameplate value at start-up \n5) Construction Start Year \n6) First Grid Connection Date (YYYY-MM-DD)\n7) Commercial Operation Date (YYYY-MM-DD)  \n8) Current Status (Operational, Long-term-Outage, Permanent Shutdown as of 31 Dec 2024)\nIf any data point cannot be located in publicly available sources, enter “NA”.\nIf a nuclear plant has different units, output different units like Taishan-1, Taishan-2.\n\nPlease deliver the results in one Markdown table with the columns in this exact order:\nReactor Name |  Country | Reactor Type | Net Capacity (MW) | Construction Start Year | First Grid Connection Date | Commercial Operation Date| Current Status\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"reactorname\"], \"required\": [\"reactorname\", \"country\", \"reactortype\", \"netcapacity(mw)\", \"constructionstartyear\", \"firstgridconnectiondate\", \"commercialoperationdate\", \"currentstatus\"], \"eval_pipeline\": {\"constructionstartyear\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"netcapacity(mw)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"firstgridconnectiondate\": {\"preprocess\": [\"norm_date\"], \"metric\": [\"date_near\"]}, \"commercialoperationdate\": {\"preprocess\": [\"norm_date\"], \"metric\": [\"date_near\"]}, \"reactorname\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"country\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"reactortype\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"currentstatus\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
+{"instance_id":"ws_en_050","query":"For a comparative politics study on descriptive representation, compile the share of women in the lower or single chamber of every OECD member’s national parliament at four benchmark dates: 31 Dec 1995, 31 Dec 2005, 31 Dec 2015, and 31 Dec 2024.\nExclude upper chambers. Regarding the total seats, please provide the total number of seats in the lower parliament.  If a country joined the OECD after 1995, record 'NA' for all statistics for years before membership.\n\nPlease output one Markdown table with the columns, in this exact order:\n Country | Share of Women Seats 1995(%) | Total Seats 1995 | Share of Women Seats 2005(%) | Total Seats 2005 | Share of Women Seats 2015(%) | Total Seats 2015 |  Share of Women Seats 2024(%) | Total Seats 2024 \n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"country\"], \"required\": [\"country\", \"shareofwomenseats1995(%)\", \"totalseats1995\", \"shareofwomenseats2005(%)\", \"totalseats2005\", \"shareofwomenseats2015(%)\", \"totalseats2015\", \"shareofwomenseats2024(%)\", \"totalseats2024\"], \"eval_pipeline\": {\"shareofwomenseats1995(%)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"totalseats1995\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"shareofwomenseats2005(%)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"totalseats2005\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"shareofwomenseats2015(%)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"totalseats2015\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"shareofwomenseats2024(%)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"totalseats2024\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"country\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
+{"instance_id":"ws_en_051","query":"I’m interested in physics. Could you please provide a list of the Best 50 U.S. STEM High Schools 2024 according to 2024 U.S. News & World Report, including each school’s location (state), its STEM High Schools ranking, National Rankings position, State Rankings position, the grades it serves, and the total number of AP courses offered (as listed on NICHE) as reported for 2024? \nPlease output the organized data in one Markdown table format.\nThe column names should be in order as follows:                                                                                                                                                                  \nHigh School, States, STEM High Schools ranking, National Rankings, State Rankings, Served Grades, AP Courses.\nInstructions:\n1. Use only Arabic numerals for all rankings.  \n2. Write out the full state name—no abbreviations are permitted.  \n3. If any information is missing, enter \"NA.\"  \n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"highschool\"], \"required\": [\"highschool\", \"states\", \"stemhighschoolsranking\", \"nationalrankings\", \"staterankings\", \"servedgrades\", \"apcourses\"], \"eval_pipeline\": {\"stemhighschoolsranking\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"nationalrankings\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"apcourses\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"highschool\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"states\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"servedgrades\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"staterankings\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
+{"instance_id":"ws_en_052","query":"For an infrastructure-finance paper, I need to benchmark capital intensity of large offshore wind assets. List every offshore wind farm in European waters that was fully commissioned from 2010-01-01 to 2024-12-31 and whose nameplate capacity is ≥ 300 MW. Ignore projects still under construction or phases that are only partially energized by 2024.\n\n\nPlease output one Markdown table with the columns, in this exact order:\n Wind Farm | Sea \/ Basin | Capacity (MW) | Turbines Number | Turbine Model | Commissioning Year | Owner \/ Operator\nFill missing fields with “NA”.\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```","evaluation":"{\"unique_columns\": [\"windfarm\"], \"required\": [\"windfarm\", \"sea\/basin\", \"capacity(mw)\", \"turbinesnumber\", \"turbinemodel\", \"commissioningyear\", \"owner\/operator\"], \"eval_pipeline\": {\"commissioningyear\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"turbinesnumber\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"capacity(mw)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"windfarm\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"sea\/basin\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"turbinemodel\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\nthe level of detail in the model description may differ from that in the reference answer, as long as it can be inferred to refer to the same model.\"}, \"owner\/operator\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\nthe company mentioned in the answercould be a subset of the reference answer\"}}}","language":"en"}
+{"instance_id":"ws_en_053","query":"I am preparing a policy brief on post-millennial commercial nuclear deployment and need a consolidated snapshot of every civil nuclear power reactor worldwide that entered commercial operation between 1 Jan 2008 and 31 Dec 2024.\n To keep the sample coherent, please exclude: research or prototype reactors, small modular units ≤ 300 MW(e) net capacity, and reactors that were permanently shut down before reaching commercial service.\n For each qualifying reactor, compile the following eight factual attributes—these data points are typically scattered across different industry reports (e.g., IAEA PRIS, WNA country profiles, corporate annual reports) and cannot all be gathered from a single page:\n1) Reactor Name (use the operator’s official English designation)  \n2)Country \n3) Reactor Type (e.g., PWR, BWR, VVER-1200, APR-1400, etc.) \n4) Net Electrical Capacity (MW) — Reference Unit Power\n5) Construction Start Year \n6) First Grid Connection Date (YYYY-MM-DD)\n7) Commercial Operation Date (YYYY-MM-DD)  \n8) Current Status (Operational, Long-term-Outage, Permanent Shutdown as of 31 Dec 2024)\nIf any data point cannot be located in publicly available sources, enter “NA”.\nIf a nuclear plant has different units, output different units like Taishan-1, Taishan-2.\n\nPlease deliver the results in one Markdown table with the columns in this exact order:\nReactor Name |  Country | Reactor Type | Net Capacity (MW) | Construction Start Year | First Grid Connection Date | Commercial Operation Date| Current Status\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"reactorname\"], \"required\": [\"reactorname\", \"country\", \"reactortype\", \"netcapacity(mw)\", \"constructionstartyear\", \"firstgridconnectiondate\", \"commercialoperationdate\", \"currentstatus\"], \"eval_pipeline\": {\"constructionstartyear\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"netcapacity(mw)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"firstgridconnectiondate\": {\"preprocess\": [\"norm_date\"], \"metric\": [\"date_near\"]}, \"commercialoperationdate\": {\"preprocess\": [\"norm_date\"], \"metric\": [\"date_near\"]}, \"reactorname\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"country\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"reactortype\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"currentstatus\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
 {"instance_id":"ws_en_054","query":"List every mega airport project commenced earlier than 31 Dec 2024 (including 31 Dec 2024)  and later than 1 Jan 2010 (including 1 Jan 2010), with a price tag of more than 1 billion dollars. All airport projects included in the statistics need to be overall projects.\nI need the Airport Name, State, Construction Start Year, Completion Year, Final Total Cost (USD bn, adjusted cost at the finished year). The airports should be in the following states: Utah, New York, Missouri, Pennsylvania, Colorado, Texas, Tennessee, New Jersey, Oregon, Louisiana.\n\nProvide one Markdown table with the following columns (in the exact order):\n Project | State |  Start Year | Completion Year | Final Total Cost \nNotes: \n If the overall project hasn't been completed by the end of 2024, just mark the Completion column as NA, and its final cost should be presented with the estimated price tag in 2024. The number should be marked with an asterisk (*) to denote that it is an estimate. \n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```","evaluation":"{\"unique_columns\": [\"project\"], \"required\": [\"project\", \"state\", \"startyear\", \"completionyear\", \"finaltotalcost\"], \"eval_pipeline\": {\"completionyear\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"finaltotalcost\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"startyear\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\n A one-year gap between the answer and reference answer is allowed.\"}, \"project\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\n\\\"new terminal\\\" or \\\"terminal expansion\\\" can be considered as the same meaning\"}, \"state\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
-{"instance_id":"ws_en_055","query":"Retrieve the details about, Location (Country, City), Owning Entity\/Company, and Attendance( 2023, in thousands) of the top 25 amusement\/theme parks worldwide jointly published by AECOM and the Themed Entertainment Association (TEA)\n\nPlease organize the results in one Markdown table with the following columns:\nPark, Location, Owning Entity\/Company, Attendance (in thousands)\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"park\", \"location\"], \"required\": [\"park\", \"location\", \"owningentity\/company\", \"attendance(inthousands)\"], \"eval_pipeline\": {\"attendance(inthousands)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"park\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"location\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"owningentity\/company\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
-{"instance_id":"ws_en_056","query":"My crush is obsessed with NBC’s The Voice—he has binged Season 1~26. I’m putting together a slick cheat sheet so we can geek out together.\nFor every season, could you list:\nthe season number, premiere date (Ep 1, YYYY-MM-DD), finale date (YYYY-MM-DD), total episode count, host(s), coaches line-up (separated by comma), Mega Mentor, winner’s name, winner’s final coach, winner’s Blind-Audition song, winner’s finale song, and the winning coach’s cumulative titles after that season. If it doesn't have a Mega Mentor, please enter 'N\/A' into the table.\n\nPlease organize the results in one Markdown table with the following columns in order:\nSeason Number, Premiere Date(Ep 1, YYYY-MM-DD), Finale Date (YYYY-MM-DD), Total Episode Count, Host(s), Coaches Line-up, Mega Mentor, Winner, Winner’s Final Coach, Winner’s Blind-Audition Song, Winner’s Finale Song, Winning coach’s Cumulative Titles.\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```","evaluation":"{\"unique_columns\": [\"seasonnumber\"], \"required\": [\"seasonnumber\", \"premieredate\", \"finaledate(yyyy-mm-dd)\", \"totalepisodecount\", \"host(s)\", \"coachesline-up\", \"megamentor\", \"winner\", \"winner’sfinalcoach\", \"winner’sblind-auditionsong\", \"winner’sfinalesong\", \"winningcoach’scumulativetitles\"], \"eval_pipeline\": {\"seasonnumber\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"premieredate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"finaledate(yyyy-mm-dd)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"totalepisodecount\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"winner\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"winner’sfinalcoach\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"winningcoach’scumulativetitles\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"host(s)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"coachesline-up\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"megamentor\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"winner’sblind-auditionsong\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"winner’sfinalesong\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\nIf multiple pieces of music are provided in the reference answer, all must be include.\"}}}","language":"en"}
-{"instance_id":"ws_en_057","query":"Identify the feature-length animated films released by Walt Disney Animation Studios from January 1, 2000, through December 31, 2024. For each film, include the official theatrical release date in the U.S. in the \"Month Day, Year\" format (e.g., May 19, 2000), the awards and nominations the film itself has received. Do not include the awards or nominations won by individuals such as producers, directors, musicians and editing-related personnel.\n\nPresent the data in one Markdown table with columns: Film Title, Release Date, Nominations, Awards\n\nNote: If no award information can be found, indicate as NA.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"filmtitle\"], \"required\": [\"filmtitle\", \"releasedate\", \"nominations\", \"awards\"], \"eval_pipeline\": {\"filmtitle\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"releasedate\": {\"preprocess\": [\"norm_date\"], \"metric\": [\"date_near\"]}, \"nominations\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"awards\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
+{"instance_id":"ws_en_055","query":"Retrieve the details about, Location ( Country, City and or State\/Province), Owning Entity\/Company, and Attendance( 2023, in thousands) of the top 25 amusement\/theme parks worldwide jointly published by AECOM and the Themed Entertainment Association (TEA)\n\nPlease organize the results in one Markdown table with the following columns:\nPark, Location, Owning Entity\/Company, Attendance (in thousands)\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"park\", \"location\"], \"required\": [\"park\", \"location\", \"owningentity\/company\", \"attendance(inthousands)\"], \"eval_pipeline\": {\"attendance(inthousands)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"park\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"location\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"owningentity\/company\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
+{"instance_id":"ws_en_056","query":"My crush is obsessed with NBC’s The Voice—he has binged Season 1~26. I’m putting together a slick cheat sheet so we can geek out together.\nFor every season, could you list:\nthe season number, premiere date (Ep 1, YYYY-MM-DD), finale date (YYYY-MM-DD), total episode count, host(s), coaches line-up (separated by comma), Mega Mentor, winner’s name, winner’s final coach, winner’s Blind-Audition song, winner’s finale song, and the winning coach’s cumulative titles after that season. If it doesn't have a Mega Mentor, please enter 'N\/A' into the table.\n\nPlease organize the results in one Markdown table with the following columns in order:\nSeason Number, Premiere Date(Ep 1, YYYY-MM-DD), Finale Date (YYYY-MM-DD), Total Episode Count, Host(s), Coaches Line-up, Mega Mentor, Winner, Winner’s Final Coach, Winner’s Blind-Audition Song, Winner’s Finale Song, Winning coach’s Cumulative Titles.\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```","evaluation":"{\"unique_columns\": [\"seasonnumber\"], \"required\": [\"seasonnumber\", \"premieredate\", \"finaledate(yyyy-mm-dd)\", \"totalepisodecount\", \"host(s)\", \"coachesline-up\", \"megamentor\", \"winner\", \"winner’sfinalcoach\", \"winner’sblind-auditionsong\", \"winner’sfinalesong\", \"winningcoach’scumulativetitles\"], \"eval_pipeline\": {\"seasonnumber\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"premieredate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"finaledate(yyyy-mm-dd)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"totalepisodecount\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"winner\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"winner’sfinalcoach\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"winningcoach’scumulativetitles\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"host(s)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"coachesline-up\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\n individuals labeled as guest or comeback stage ones are not necessary.\"}, \"megamentor\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"winner’sblind-auditionsong\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"winner’sfinalesong\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\nIf multiple pieces of music are provided in the reference answer, all must be include.\"}}}","language":"en"}
+{"instance_id":"ws_en_057","query":"Identify the feature-length animated films produced by Walt Disney Animation Studios and released in the U.S. regular theaters from January 1, 2000 through December 31, 2024 (inclusively). For each film, include the official theatrical release date (not the premiere date) in the U.S. in the \"Month Day, Year\" format (e.g., May 19, 2000), the awards and nominations the film itself has received. Do not include the awards or nominations won by individuals such as company, producers, directors, musicians and editing-related personnel.\n\nPresent the data in one Markdown table with columns: Film Title, Release Date, Nominations, Awards\n\nNote: If no award information can be found, indicate as NA.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"filmtitle\"], \"required\": [\"filmtitle\", \"releasedate\", \"nominations\", \"awards\"], \"eval_pipeline\": {\"filmtitle\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"releasedate\": {\"preprocess\": [\"norm_date\"], \"metric\": [\"date_near\"]}, \"nominations\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"awards\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
 {"instance_id":"ws_en_058","query":"Could you please give me a demographic brief analyzing the shifting racial composition of new U.S. citizens since 2002? This requires a year-by-year breakdown of their original birthplace composition from 2002 to 2022. Using U.S. Department of Homeland Security Yearbook of Immigration Statistics, the Annual Flow Reports, and archived USCIS Factsheets, compile the following metrics for each fiscal year from 2002 through 2022 (including 2022). If a statistic is unavailable, enter NA.\n\nPlease output the results in one Markdown table with columns, in this exact order:\nYear | Total Naturalized | Born in Africa | Born in Asia | Born in Europe | Born in Caribbean | Born in Central America| Born in other North America | Born in Oceania | Born in South America\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```","evaluation":"{\"unique_columns\": [\"year\"], \"required\": [\"year\", \"totalnaturalized\", \"borninafrica\", \"borninasia\", \"bornineurope\", \"bornincaribbean\", \"bornincentralamerica\", \"borninothernorthamerica\", \"borninoceania\", \"borninsouthamerica\"], \"eval_pipeline\": {\"totalnaturalized\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"borninafrica\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"borninasia\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"bornineurope\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"bornincaribbean\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"bornincentralamerica\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"borninothernorthamerica\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"borninoceania\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"borninsouthamerica\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"year\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"en"}
-{"instance_id":"ws_en_059","query":"I need to verify the basic information for all TED Prize winners from 2005 to 2015, including: award year, laureate name, corresponding TED talk title, event city.\n\nPlease present the compiled data in one Markdown table. The column headers, in order, should be: Award Year, Laureate, Talk Title, Event City.\n \nIf any field cannot be located in publicly available sources, fill it with NA.\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```","evaluation":"{\"unique_columns\": [\"laureate\"], \"required\": [\"awardyear\", \"laureate\", \"talktitle\", \"eventcity\"], \"eval_pipeline\": {\"awardyear\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"laureate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"talktitle\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"eventcity\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
+{"instance_id":"ws_en_059","query":"I need to verify the basic information for all TED Prize winners from 2005 to 2015, including: award year, laureate (the individual or idea that has received the honor), corresponding TED talk title, and event city.\n\nPlease present the compiled data in one Markdown table. The column headers, in order, should be: Award Year, Laureate, Talk Title, Event City.\n \nIf any field cannot be located in publicly available sources, fill it with NA.\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```","evaluation":"{\"unique_columns\": [\"laureate\"], \"required\": [\"awardyear\", \"laureate\", \"talktitle\", \"eventcity\"], \"eval_pipeline\": {\"awardyear\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"laureate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"talktitle\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"eventcity\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
 {"instance_id":"ws_en_060","query":"List the annual global top 1-top 10 grossing games downloaded via iPhone & iPad by AppMagic from 2020-2024 (including 2024), with the official English name, the publisher, and the first official release date.\n\nPlease organize the results in one Markdown table with the following column names in order:\nYear, Ranking, Game, Publisher, Release Date\nPlease list Release Date in the format of yyyy-mm-dd\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```","evaluation":"{\"unique_columns\": [\"year\", \"ranking\"], \"required\": [\"year\", \"ranking\", \"game\", \"publisher\", \"releasedate\"], \"eval_pipeline\": {\"year\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"ranking\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"game\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"releasedate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"publisher\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
 {"instance_id":"ws_en_061","query":"Please compile the names of every individual who has served as a chief executive Premier (state) or Chief Minister (mainland territory) of Australia at any time between 2015-01-01 and 2024-12-31 (including 2015-01-01 and 2024-12-31).\nListing for each person: (1) Name, (2) State\/Territory, (3) Office \/ Position (Premier \/ Chief Minister), (4) Party abbreviation, (5) Date first sworn in during the period, (6) Date finally left office during the period – or “incumbent” if still serving on 2024-12-31, (7) Mode of accession (general election \/ party leadership change \/ acting succession\/ other), (8) Reason for leaving (election defeat \/ resignation \/ party coup \/ other).\n\nPlease output the organized data in the format of one Markdown table.\nThe column names are as follows:\nName, State\/Territory, Office \/ Position, Party, Start Date, End Date, Accession Mode, Leaving Reason\nNotes:\n- The dates should be provided in the yyyy-mm-dd format, such as 2022-02-01. If they are still in office in 2024, please indicate by writing \"incumbent\", and use \"-\" for their leaving reason.\n- Please use the party abbreviation for the column Party.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```","evaluation":"{\"unique_columns\": [\"name\"], \"required\": [\"name\", \"state\/territory\", \"office\/position\", \"party\", \"startdate\", \"enddate\", \"accessionmode\", \"leavingreason\"], \"eval_pipeline\": {\"office\/position\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"party\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"accessionmode\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"leavingreason\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"startdate\": {\"preprocess\": [\"norm_date\"], \"metric\": [\"date_near\"]}, \"enddate\": {\"preprocess\": [\"norm_date\"], \"metric\": [\"date_near\"]}, \"name\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"state\/territory\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
 {"instance_id":"ws_en_062","query":"Please help me compile the founding year, governing authority, official website addresses of all Russell Group universities in the UK, as well as their rankings in the 2025 Guardian University Guide and the 2026 QS World University Rankings.\n\nPlease output the compiled data in the format of one Markdown table.\nThe column names in the table are as follows in sequence: University, Founding Year, Governing Body, Official Website Address, 2025 UK Guardian Rankings, 2026 QS World Rankings.\n\nAmong them:\nThe full name of the university should be written, for example: University of Cambridge;\n\"Founding Year\" refers to the official date on which the institution was recognized as a university—most commonly the year it received its Royal Charter or parliamentary approval.\nThe full name of the governing authority should be written, for example: University Council; if there are multiple governing authorities, please separate them with commas;\nThe official website address should be complete, for example: https:\/\/www.ox.ac.uk\/\nThe rankings in the lists should be in Arabic numerals, for example: 11.\n\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```","evaluation":"{\"unique_columns\": [\"university\"], \"required\": [\"university\", \"foundingyear\", \"governingbody\", \"officialwebsiteaddress\", \"2025ukguardianrankings\", \"2026qsworldrankings\"], \"eval_pipeline\": {\"university\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"foundingyear\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"2025ukguardianrankings\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"2026qsworldrankings\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"governingbody\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"officialwebsiteaddress\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
@@ -65,16 +65,16 @@
 {"instance_id":"ws_en_065","query":"Curious about the birth rate, death rate, and the overall population of the United States. Please gather the following information on yearly basis from 2012-2023 (including 2012 and 2023). \n\nPlease organize the results in one Markdown table with the following columns:\nYear, Total Population(Million), Male Population(Million), Female Population(Million), Birth Rate, Death Rate\nNote: both birth rate and death rate indicate the number of live births\/deaths occurring during the year, per 1,000 population. \n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"year\"], \"required\": [\"year\", \"totalpopulation(million)\", \"malepopulation(million)\", \"femalepopulation(million)\", \"birthrate\", \"deathrate\"], \"eval_pipeline\": {\"totalpopulation(million)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"malepopulation(million)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"femalepopulation(million)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"birthrate\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"deathrate\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"year\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"en"}
 {"instance_id":"ws_en_066","query":"I am analyzing the regional adaptation of Dyson’s Supersonic hair dryers (consumer edition) since their first launch. Please compile the official launch information and core specifications for every consumer-facing Supersonic model (excluding professional “HD Pro” salon  version) released between April 2016 and December 2024 in the U.S., China, and Japan. Omit any special-edition colorways that did not introduce new hardware. For each qualifying model–region pair, capture the following: Region (U.S., China, Japan), Model Code (e.g., HD01, HD03), First Retail Availability Date (in format YYYY-MM), Launch MSRP, Number of Included Attachments in a standard set, Warranty Term in years. \n\n\nAnd present the compiled data in one Markdown table with following column names：Region , Model Code, First Retail Availability Date, Launch MSRP, Number of Included Attachments, Warranty(yrs).\nIf any field is unavailable, enter NA.\nLaunch MSRPs are launch-time official prices in the local market currency.\nWhen filling in the model, only two letters and numbers are required, for example: HD01.\nUse only Arabic numerals in Number of Included Attachments and Warranty(yrs)\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"region\", \"modelcode\"], \"required\": [\"region\", \"modelcode\", \"firstretailavailabilitydate\", \"launchmsrp\", \"numberofincludedattachments\", \"warranty(yrs)\"], \"eval_pipeline\": {\"numberofincludedattachments\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"warranty(yrs)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"firstretailavailabilitydate\": {\"preprocess\": [\"norm_date\"], \"metric\": [\"date_near\"]}, \"launchmsrp\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"region\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"modelcode\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"en"}
 {"instance_id":"ws_en_067","query":"Please gather data on girls’ single-sex high schools operating in Seoul as of December 2024. For each school, provide the following details: school name, year founded, public or private operation, and Total Students (2023\/2024). For the number of students, give the number of students in 2024. If there is no updated information of 2024, it's sufficient to give the data from 2023 or 2022.\n\nPresent the information in one Markdown table with the following column names:\nSchool, Year Founded, Public\/Private, Total Students.\nIf any data is unavailable, indicate NA.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"school\"], \"required\": [\"school\", \"yearfounded\", \"public\/private\", \"totalstudents\"], \"eval_pipeline\": {\"school\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"yearfounded\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"public\/private\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"totalstudents\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}}}","language":"en"}
-{"instance_id":"ws_en_068","query":"Please provide a table of the books on the \"Ten Best Books List\" of The New York Times for the years 2022, 2023, and 2024. The table should include five items: year, book title, author (if there are multiple authors, separate them with commas, but don't include translators or narrators), first publisher (if there is a specific imprint under the publisher, please specify it), and first publication time (in the format of yyyy-mm-dd, for example, 2024-01-01; if there is no specific date, yyyy-mm is also acceptable). \n\nPlease output the organized data in the format of one Markdown table and only record the information for the English edition of these books.\nThe column names in the table are as follows, in sequence:\nYear, Book Title, Author, First Publisher, First Publication Time\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is \n```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"booktitle\"], \"required\": [\"year\", \"booktitle\", \"author\", \"firstpublisher\", \"firstpublicationtime\"], \"eval_pipeline\": {\"year\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"firstpublicationtime\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"author\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"firstpublisher\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"booktitle\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"en"}
-{"instance_id":"ws_en_069","query":"I am conducting film research and need you to list the top five highest-grossing domestic films in the United States for each year from 2020 to 2024 (including 2024). \n\nPlease provide the organized data in one Markdown table format.\nThe column headers, in order, should be:\nYear, Film, Director, Gross Domestic Box Office (USD Billion), Genre.\nNote: For films released at the end of December, if most of their box office revenue was generated in the following year, they should be classified under the following year's list.\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"film\"], \"required\": [\"year\", \"film\", \"director\", \"grossdomesticboxoffice(usdbillion)\", \"genre\"], \"eval_pipeline\": {\"year\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"film\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"grossdomesticboxoffice(usdbillion)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"director\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"genre\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\n It is acceptable if the response to be evaluated intersects with the reference answer.\"}}}","language":"en"}
-{"instance_id":"ws_en_070","query":"My child is about to finish undergraduate studies and is planning to pursue a master's degree in Hong Kong, specifically in a business school. Please help me compile a comparison table for 2025 covering The University of Hong Kong (HKU), The Chinese University of Hong Kong (CUHK), and The Hong Kong University of Science and Technology (HKUST). The table should include relevant business-related programs (excluding MBA), entry requirements, and tuition fees (in HKD).\nPlease exclude joint programs with foreign universities and dual-degree\/master’s programs, as we are only interested in solid, stand-alone degrees.\n\nPlease present the organized data in one Markdown table format.\nThe column headers should be, in order:\nUniversity, Major, Tuition, Requirements.\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"university\", \"major\"], \"required\": [\"university\", \"major\", \"tuition\", \"requirements\"], \"eval_pipeline\": {\"requirements\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"tuition\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\nIt is acceptable as long as the number in the response to be evaluated is the same as that in the reference answer, even if additional information is provided.\"}, \"university\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"major\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"en"}
+{"instance_id":"ws_en_068","query":"Please provide a table of the books on the \"Ten Best Books List\" of The New York Times for the years 2022, 2023, and 2024. The table should include five items: year, book title, author (if there are multiple authors, separate them with commas, but don't include translators or narrators), first publisher (if there is a specific imprint under the publisher, please specify it), and first publication time  in English (in the format of yyyy-mm-dd, for example, 2024-01-01; if there is no specific date, yyyy-mm is also acceptable). \n\nPlease output the organized data in the format of one Markdown table and only record the information for the English edition of these books.\nThe column names in the table are as follows, in sequence:\nYear, Book Title, Author, First Publisher, First Publication Time\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is \n```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"booktitle\"], \"required\": [\"year\", \"booktitle\", \"author\", \"firstpublisher\", \"firstpublicationtime\"], \"eval_pipeline\": {\"year\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"firstpublicationtime\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"author\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"firstpublisher\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"booktitle\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"en"}
+{"instance_id":"ws_en_069","query":"I am conducting film research and need you to list the top five domestic films in  domestic calendar grosses in the United States for each year from 2020 to 2024 (including 2024). \n\nPlease provide the organized data in one Markdown table format.\nThe column headers, in order, should be:\nYear, Film, Director, Lifetime Gross in Domestic Box Office (USD Billion), Genre.\nNote: For films released at the end of December, if most of their box office revenue was generated in the following year, they should be classified under the following year's list.\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"film\"], \"required\": [\"year\", \"film\", \"director\", \"lifetimegrossindomesticboxoffice(usdbillion)\", \"genre\"], \"eval_pipeline\": {\"year\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"film\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"lifetimegrossindomesticboxoffice(usdbillion)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"director\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"genre\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\n It is acceptable if the response to be evaluated intersects with the reference answer.\"}}}","language":"en"}
+{"instance_id":"ws_en_070","query":"My child is about to finish undergraduate studies and is planning to pursue a full-time master's degree in Hong Kong, specifically in a business school. Please help me compile a comparison table for 2025 covering The University of Hong Kong (HKU), The Chinese University of Hong Kong (CUHK), and The Hong Kong University of Science and Technology (HKUST). The table should include relevant business-related programs (excluding MBA), entry requirements, and tuition fees (in HKD).\nPlease exclude joint programs with foreign universities and dual-degree\/master’s programs, as we are only interested in solid, stand-alone degrees. Also exclude the programs those are subject to University's Approval.\n\nPlease present the organized data in one Markdown table format.\nThe column headers should be, in order:\nUniversity, Major, Tuition, Requirements.\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"university\", \"major\"], \"required\": [\"university\", \"major\", \"tuition\", \"requirements\"], \"eval_pipeline\": {\"requirements\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"tuition\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\nIt is acceptable as long as the number in the response to be evaluated is the same as that in the reference answer, even if additional information is provided.\"}, \"university\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"major\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"en"}
 {"instance_id":"ws_en_071","query":"Please list the top 10 individuals from the Forbes Global Billionaires Ranking for each year from 2019 to 2024 (including 2024). For each person, include their name, ranking, net worth, source of wealth, and age of that year end. Present the information in a table format. All your data should come from Forbes, which means the ages and net worth are calculated based on the publication date.\n\nPlease present the organized data in one Markdown table format.\nThe column headers, in order, should be:\nYear, Rank, Name, Net Worth (in USD billions), Age, Source of Wealth.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"name\", \"year\"], \"required\": [\"year\", \"rank\", \"name\", \"networth(inusdbillions)\", \"age\", \"sourceofwealth\"], \"eval_pipeline\": {\"year\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"rank\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"age\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"networth(inusdbillions)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"name\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"sourceofwealth\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
-{"instance_id":"ws_en_072","query":"For a paper on U.S. politics, please list the U.S. presidents and vice presidents who won in each presidential election since the founding of the country. If any data is unavailable, please use \"\/\" as a placeholder.\n\nPlease present the organized data in one Markdown table format.\nThe column headers should be, in order:\nYear, President, Vice President, Party, Popular Vote Count, Electoral Vote Count.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.`","evaluation":"{\"unique_columns\": [\"year\", \"president\"], \"required\": [\"year\", \"president\", \"vicepresident\", \"party\", \"popularvotecount\", \"electoralvotecount\"], \"eval_pipeline\": {\"year\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"electoralvotecount\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"popularvotecount\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"vicepresident\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"party\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"president\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
-{"instance_id":"ws_en_073","query":"Please help me sort out a list of NVIDIA's desktop graphics card products released from 1990 to 2024 (including 1990 and 2024), including the specific product series, product names, chip names, release dates, bus interfaces, memory capacities, memory types, memory bit widths, core frequencies (base frequencies), and memory frequencies.\n\nPlease output the sorted data in the format of one Markdown table. The column names in the table are: Product Series, Product Name, Chip Name, Release Date, Bus Interface, Memory Capacity, Memory Type, Memory Bit Width, Core Frequency, Memory Frequency.\nRelease Date should be formatted as Sep 12th, 2012\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"productname\"], \"required\": [\"productseries\", \"productname\", \"chipname\", \"releasedate\", \"businterface\", \"memorycapacity\", \"memorytype\", \"memorybitwidth\", \"corefrequency\", \"memoryfrequency\"], \"eval_pipeline\": {\"productname\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"chipname\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"releasedate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"businterface\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"memorytype\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"corefrequency\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"memoryfrequency\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"productseries\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"memorybitwidth\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\n Under the same product name, there may be different Memory Bit Widths. It is considered correct regardless the responses are written separately or together.\\nMemory Capacity： Under the same product name, there may be different Memory Capacities. It is considered correct regardless the responses are written separately or together.\"}, \"memorycapacity\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
-{"instance_id":"ws_en_074","query":"Please track the performance of far-right parties in the United Kingdom, France, and Germany in all presidential (first round) elections held from 2010 through 2024 (including 2024). For each election, record the following details: Year, Country, Party, Party President, Popular Vote (number of votes won by the party or its presidential candidate), Vote Share (%, to keep one digit), Winning Party \/ bloc(overall victor of the election)\n\nPlease output the organized data in the format of one Markdown table.\nThe column names in the table are as follows in sequence:\nYear, Country, Party, Party President, Popular Vote, Vote Share(%), Winning Party\/ Bloc\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"year\", \"country\", \"party\"], \"required\": [\"year\", \"country\", \"party\", \"partypresident\", \"popularvote\", \"voteshare(%)\", \"winningparty\/bloc\"], \"eval_pipeline\": {\"year\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"popularvote\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"voteshare(%)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"country\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"party\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"partypresident\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"winningparty\/bloc\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
+{"instance_id":"ws_en_072","query":"For a paper on U.S. politics, please list the U.S. presidents and vice presidents who won in each presidential election since the founding of the country until 2025. If any data is unavailable, please use \"-\" as a placeholder.\n\nPlease present the organized data in one Markdown table format.\nThe column headers should be, in order:\nYear, President, Vice President, Party, Popular Vote Count, Electoral Vote Count.\nNotes: If the president did not represent any political party, list as \"Independent\".\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.`","evaluation":"{\"unique_columns\": [\"year\", \"president\"], \"required\": [\"year\", \"president\", \"vicepresident\", \"party\", \"popularvotecount\", \"electoralvotecount\"], \"eval_pipeline\": {\"year\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"electoralvotecount\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"popularvotecount\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"vicepresident\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"party\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"president\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
+{"instance_id":"ws_en_073","query":"Please help me sort out a list of NVIDIA's desktop graphics card products released from 1990 to 2024 (including 1990 and 2024), including the specific product series, product names, chip names, release dates, bus interfaces, memory capacities, memory types, memory bit widths, core frequencies (base frequencies), and memory frequencies.\n\nPlease output the sorted data in the format of one Markdown table. The column names in the table are: Product Series, Product Name, Chip Name, Release Date, Bus Interface, Memory Capacity, Memory Type, Memory Bit Width, Core Frequency, Memory Frequency.\nRelease Date should be formatted as Sep 12th, 2012. If the same graphics card product is based on multiple chips (graphics processor), list each chips in a row.\nchips\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"productname\", \"chipname\"], \"required\": [\"productseries\", \"productname\", \"chipname\", \"releasedate\", \"businterface\", \"memorycapacity\", \"memorytype\", \"memorybitwidth\", \"corefrequency\", \"memoryfrequency\"], \"eval_pipeline\": {\"productname\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"chipname\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"releasedate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"businterface\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"memorytype\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"corefrequency\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"memoryfrequency\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"productseries\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"memorybitwidth\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\n Under the same product name, there may be different Memory Bit Widths. It is considered correct regardless the responses are written separately or together.\\nMemory Capacity： Under the same product name, there may be different Memory Capacities. It is considered correct regardless the responses are written separately or together.\"}, \"memorycapacity\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
+{"instance_id":"ws_en_074","query":"Please track the performance of political parties labelled as far-right in the United Kingdom, France, and Germany in all presidential (first round) elections held from 2010 through 2024 (including 2024). For each election, record the following details: Year, Country, Party, Party President during the election (if the party does not has a pressent, record the leader(s)\/chairman), Popular Vote (number of votes won by the party or its presidential candidate), Vote Share (%, to keep one digit), Winning Party \/ bloc (overall victor of the election)\n\nPlease output the organized data in the format of one Markdown table.\nThe column names in the table are as follows in sequence:\nYear, Country, Party, Party President, Popular Vote, Vote Share(%), Winning Party\/ Bloc\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"year\", \"country\", \"party\"], \"required\": [\"year\", \"country\", \"party\", \"partypresident\", \"popularvote\", \"voteshare(%)\", \"winningparty\/bloc\"], \"eval_pipeline\": {\"year\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"popularvote\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"voteshare(%)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"country\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"party\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"partypresident\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"winningparty\/bloc\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
 {"instance_id":"ws_en_075","query":"It’s now June 2025, and I’ve recently become fascinated with rap music. I’d like to know what rap-related categories have been awarded at the Grammys over the past 30 years, and for each winning entry, I need the following information: Award Category, Winning Song\/Album, Performer(s) (including featured artists, listed in the official order—e.g., Killer Mike (ft. André 3000, Future & Eryn Allen Kane)). Please provide a complete list based on official Grammy records.\n\nPlease present the organized data in one Markdown table format.\nThe column headers should be, in order:\nAnnual, Award Category, Winning Song\/Album, Performer(s).\nFor the 62nd annual award, just output 62 in the column of \"Annual\".\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"annual\", \"awardcategory\", \"winningsong\/album\"], \"required\": [\"annual\", \"awardcategory\", \"winningsong\/album\", \"performer(s)\"], \"eval_pipeline\": {\"performer(s)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\n The answer to be evaluated is considered correct if it mentioned one of the main performancer(s), the featuring artist(s) may be omitted\"}, \"annual\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"awardcategory\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"winningsong\/album\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"en"}
 {"instance_id":"ws_en_076","query":"Please list the specific military expenditure (in billions of US dollars, such as 900, without decimals), GDP of the year (in trillions of US dollars, accurate to one decimal place), global ranking of military expenditure of the year, and the president\/Prime Minister (head of state) and defense minister of the United States, Russia, Germany, India, and Japan from 2019 to 2024 (including 2019 and 2024) based on the statistical data of the Stockholm Institute. (In case of changes, the person who served the longest in the year shall prevail.)\n\nPlease present the organized data in one Markdown table format.\nThe column headers should be, in order:\nYear, Country, Global Ranking, Military Expenditure (Billion), GDP (Trillion), President\/Prime Minister, Minister of Defense.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"year\", \"country\"], \"required\": [\"year\", \"country\", \"globalranking\", \"militaryexpenditure(billion)\", \"gdp(trillion)\", \"president\/primeminister\", \"ministerofdefense\"], \"eval_pipeline\": {\"globalranking\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"militaryexpenditure(billion)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"gdp(trillion)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"president\/primeminister\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"ministerofdefense\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"year\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"country\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"en"}
-{"instance_id":"ws_en_077","query":"How many changes occurred among cabinet secretaries and cabinet-level officials during Trump's first term, such as those who left their positions voluntarily or involuntarily before the term ended? What were the start and end dates of these officials, and who were their successors (note: changes of acting ministers are not included, and successors should be official appointees rather than acting ministers; if there was no official successor, please indicate \"-\").\n\nPlease present the organized data in Markdown table format.\n The column headers should be, in order:\nName, Position, Start Date, End Date, Successor.\nPlease use the YYYY-MM-DD format for Start Date, End Date, for example: 2015-01-01\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"name\", \"position\"], \"required\": [\"name\", \"position\", \"startdate\", \"enddate\", \"successor\"], \"eval_pipeline\": {\"startdate\": {\"preprocess\": [\"norm_date\"], \"metric\": [\"date_near\"]}, \"enddate\": {\"preprocess\": [\"norm_date\"], \"metric\": [\"date_near\"]}, \"name\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"successor\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"position\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
+{"instance_id":"ws_en_077","query":"How many changes occurred among cabinet secretaries and cabinet-level officials during Trump's first term, such as those who left their positions voluntarily or involuntarily before the term ended? What were the start and end dates of these officials, and who were their successors (note: changes of acting ministers are not included, and successors should be official appointees rather than acting ministers; if there was no official successor during Trump's first term, please indicate \"-\").\n\nPlease present the organized data in Markdown table format.\n The column headers should be, in order:\nName, Position, Start Date, End Date, Successor.\nPlease use the YYYY-MM-DD format for Start Date, End Date, for example: 2015-01-01\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"name\", \"position\"], \"required\": [\"name\", \"position\", \"startdate\", \"enddate\", \"successor\"], \"eval_pipeline\": {\"startdate\": {\"preprocess\": [\"norm_date\"], \"metric\": [\"date_near\"]}, \"enddate\": {\"preprocess\": [\"norm_date\"], \"metric\": [\"date_near\"]}, \"name\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"successor\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"position\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
 {"instance_id":"ws_en_078","query":"I am conducting a research project on the growth trajectory of female power and would like to know which women have won the Nobel Prize in Literature, as well as their first three published works (both names and works should retain the original native language).\n\nPlease present the organized data in Markdown table format.\n The column headers should be, in order:\n Award Year, Name, Published Work, Year of First Publication.\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"publishedwork\"], \"required\": [\"awardyear\", \"name\", \"publishedwork\", \"yearoffirstpublication\"], \"eval_pipeline\": {\"yearoffirstpublication\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"awardyear\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"name\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"publishedwork\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
 {"instance_id":"ws_en_079","query":"I would like to know the operating performance in 2024 of the companies granted casino concession contracts by the Macau government. Specifically, I am interested in each company's net gaming revenue and non-gaming revenue, both expressed in millions with one decimal place with HKD, and the currency clearly indicated. Additionally, for each company’s associated five-star deluxe hotels or resorts recognized by Macao Government Tourism Office (if applicable), I would like to know the number of rooms, occupancy rate, average daily rate (ADR), and revenue per available room (RevPAR). If some data is unavailable, the corresponding fields should be marked with \"-\".\n\nPlease output the sorted data with each Luxury 5-Star Hotel\/Resort hotel as a row in the format of one Markdown table. The column names in the table are as follows:\n\nYear, Gambling Enterprise, Net Gambling Revenue (in millions), Non-Gambling Revenue (in millions), 5-Star Deluxe Hotels\/Resorts, Number of Rooms, Occupancy Rate, Average Room Rate, Revenue per Available Room\n\nNet Gambling Revenue (in millions), Non-Gambling Revenue (in millions), Average Room Rate, Revenue per Available Room are all in Hong Kong dollars. For conversions between Hong Kong dollars and US dollars, please use the given exchange rate: 1 US dollar = 7.85 Hong Kong dollars.\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"5-stardeluxehotels\/resorts\"], \"required\": [\"year\", \"gamblingenterprise\", \"netgamblingrevenue(inmillions)\", \"non-gamblingrevenue(inmillions)\", \"5-stardeluxehotels\/resorts\", \"numberofrooms\", \"occupancyrate\", \"averageroomrate\", \"revenueperavailableroom\"], \"eval_pipeline\": {\"netgamblingrevenue(inmillions)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"non-gamblingrevenue(inmillions)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"numberofrooms\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"occupancyrate\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"averageroomrate\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"revenueperavailableroom\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"gamblingenterprise\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"5-stardeluxehotels\/resorts\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"year\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"en"}
 {"instance_id":"ws_en_080","query":"I need you to help me count the number of gold medals won by delegations from all Central and South American countries in the 2024 Paris Olympics, 2020 Tokyo Olympics, and 2016 Rio Olympics, as well as the events in which these gold medals were won.  \n\nOutput fields and explanations:  \n- Olympic Games: Choose from 2024 Paris Olympics, 2020 Tokyo Olympics, and 2016 Rio Olympics without changing the names.  \n- Country: The country that won the gold medals.  \n- Number of Gold Medals: The total number of gold medals won by the country in a specific Olympic Games.  \n- Gold Medal Events: Only output the major event for each gold medal. If a major event won multiple gold medals, output \"Major Event Name (Number of Gold Medals)\", e.g., \"Football (2)\". If only one gold medal was won in the event, simply output the major event name. If a country has multiple gold medal events in an Olympic Games, merge these events into one cell and separate them with commas.\n\nPlease output the compiled data in a Markdown-formatted table.\n The column headers should be:\nOlympic Games, Country, Number of Gold Medals, Events Won\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"olympicgames\", \"country\"], \"required\": [\"olympicgames\", \"country\", \"numberofgoldmedals\", \"eventswon\"], \"eval_pipeline\": {\"olympicgames\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"country\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"numberofgoldmedals\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"eventswon\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
@@ -87,19 +87,19 @@
 {"instance_id":"ws_en_087","query":"Please sort out all African countries involved in the \"Belt and Road\" initiative and their capitals as of June 2025, and query the surface area (in sq.km, using 2022 statistics, rounded to an integer), population density (people\/sq.km of land area, using 2022 statistics, rounded to an integer), total population (in thousand, using 2023 statistics, rounded to an integer), and the proportion of merchandise trade in GDP (in percentage of GDP, using 2023 statistics, rounded to one decimal place) of these countries according to the statistics of the World Bank. \n\nPlease output the sorted data in the format of one Markdown table. The column names in the table are: Country, Capital City, Surface Area (km²), Population Density (people\/km² of land area), Total Population (thousands), Merchandise Trade (% of GDP).\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```","evaluation":"{\"unique_columns\": [\"country\"], \"required\": [\"country\", \"capitalcity\", \"surfacearea(km²)\", \"populationdensity(people\/km²oflandarea)\", \"totalpopulation(thousands)\", \"merchandisetrade(%ofgdp)\"], \"eval_pipeline\": {\"surfacearea(km²)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"populationdensity(people\/km²oflandarea)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"totalpopulation(thousands)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"merchandisetrade(%ofgdp)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"country\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"capitalcity\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
 {"instance_id":"ws_en_088","query":"Billboard publishes its year-end singles chart each year. I would like information on the Top 10 songs from 2015 to 2024, including the performers for each track. Please present the details in chronological order, starting with 2015, ending with 2024.\n\nPlease present the compiled data in one Markdown table format and each row should contain one song only.\nThe column headers should be as follows:\nYear, Rank, Song, Artist\n\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"year\", \"rank\"], \"required\": [\"year\", \"rank\", \"song\", \"artist\"], \"eval_pipeline\": {\"year\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"rank\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"song\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"artist\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
 {"instance_id":"ws_en_089","query":"I want to research Apple Inc.’s product development history by compiling a list of each generation of its flagship smartphone product line (e.g., iPhone, iPhone 3G, iPhone 3GS, … up to the iPhone 15 series) released in the U.S. market between January 9, 2007 (the original iPhone’s launch date), and December 31, 2024. \nFor each model, please provide:\n• Product name  \n• Launch year  \n• Storage capacity (separate multiple options by \/, e.g. 4GB\/8GB)  \n• Official launch price in USD (listed separately for different capacities,e.g., $199\/$299 )  \n• One key new technology or feature introduced with that generation (a widely recognized highlight promoted by Apple, such as “first to support the App Store,” “first Retina display,” or “introduction of Face ID��).  \nOnly include the standard flagship models released each September or October (i.e., the main iPhone series and their Plus\/Pro variants), excluding non-flagship lines like the SE or C series. If the launch price or key new feature cannot be confirmed, leave that field blank in the table.\n\nPlease output the sorted data in the format of one Markdown table. The column names in the table are: Product, Launch Year, Capacity, Launch Price (USD), Core New Technologies\/Features.\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```","evaluation":"{\"unique_columns\": [\"product\"], \"required\": [\"product\", \"launchyear\", \"capacity\", \"launchprice(usd)\", \"corenewtechnologies\/features\"], \"eval_pipeline\": {\"launchyear\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"product\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"capacity\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"launchprice(usd)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"corenewtechnologies\/features\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\\ndeviations from the reference answer are permitted as long as they are reasonable and factual.\"}}}","language":"en"}
-{"instance_id":"ws_en_090","query":"I want to check the recent performance of large models. Please list Gemini 2.5 Pro & Lite & Flash-lite (separate the thinking and on-thinking models), Claude 3.7 and subsequently released models until June, 2025, O3 & O3 Mini & O4 Mini, Doubao-1.5-Thinking and 1.6-Thinking, DeepSeek V3 and subsequent main released models until June 2025. The specific metrics I want to know include the model number (such as Gemini-2.5-pro), context window (such as 32K), AIME-2025 metrics, SWE verified metrics (single attempt), and tau-bench-retail and airline metrics. Please try to search for all metrics on the official website of the corresponding model, but do not fabricate them. If the official website\/official paper does not release them, the relevant metrics can be output as \"NA\".\n\nPlease output the sorted data in the format of one Markdown table. The column names in the table are: model name, company, context window, AIME 2025, SWE-bench Verified, TAU-bench-Airline, Tau-bench-Retail.\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```","evaluation":"{\"unique_columns\": [\"modelname\"], \"required\": [\"modelname\", \"company\", \"contextwindow\", \"aime2025\", \"swe-benchverified\", \"tau-bench-airline\", \"tau-bench-retail\"], \"eval_pipeline\": {\"aime2025\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"swe-benchverified\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"tau-bench-airline\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"tau-bench-retail\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"modelname\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"company\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"contextwindow\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
-{"instance_id":"ws_en_091","query":"It is said that \"Mom Su\" has saved AMD. Please help me sort out the specific processor (CPU) products released by AMD and their detailed information during the decade since Lisa Su became the CEO of AMD. If individual information cannot be retrieved from the Internet, do not make up, just output \"NA\" in the corresponding cell.\n\nPlease output the sorted data in the format of one Markdown table. The column names in the table are: Time, Product Series, Processor Model, Core Architecture, Manufacturing Process (nm), Cores, Threads, Core Frequency (GHz), L2 Cache (MB), L3 Cache (MB), Graphics Model, Number of Graphics Cores.\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is:\n```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"processormodel\"], \"required\": [\"time\", \"productseries\", \"processormodel\", \"corearchitecture\", \"manufacturingprocess(nm)\", \"cores\", \"threads\", \"corefrequency(ghz)\", \"l2cache(mb)\", \"l3cache(mb)\", \"graphicsmodel\", \"numberofgraphicscores\"], \"eval_pipeline\": {\"processormodel\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"cores\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"threads\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"corefrequency(ghz)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"l2cache(mb)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"l3cache(mb)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"numberofgraphicscores\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"time\": {\"preprocess\": [\"norm_date\"], \"metric\": [\"date_near\"]}, \"productseries\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"corearchitecture\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"manufacturingprocess(nm)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"graphicsmodel\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
+{"instance_id":"ws_en_090","query":"I want to check the recent performance of large models. Please list Gemini 2.5 Pro & Lite & Flash-lite (separate the thinking and non-thinking models), Claude 3.7 and subsequently released models until June, 2025, O3 & O3 Mini & O4 Mini, Doubao-1.5-Thinking and 1.6-Thinking, DeepSeek V3 and subsequent main released models until June 2025. The specific metrics I want to know include the model number (such as Gemini-2.5-pro), context window (such as 32K), AIME-2025 metrics, SWE verified metrics (single attempt), and tau-bench-retail and airline metrics. Please try to search for all metrics on the official website of the corresponding model, but do not fabricate them. If the official website\/official paper does not release them, the relevant metrics can be output as \"NA\".\n\nPlease output the sorted data in the format of one Markdown table. The column names in the table are: model name, company, context window, AIME 2025, SWE-bench Verified, TAU-bench-Airline, Tau-bench-Retail.\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```","evaluation":"{\"unique_columns\": [\"modelname\"], \"required\": [\"modelname\", \"company\", \"contextwindow\", \"aime2025\", \"swe-benchverified\", \"tau-bench-airline\", \"tau-bench-retail\"], \"eval_pipeline\": {\"aime2025\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"swe-benchverified\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"tau-bench-airline\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"tau-bench-retail\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"modelname\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"company\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"contextwindow\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
+{"instance_id":"ws_en_091","query":"It is said that \"Mom Su\" has saved AMD. Please help me sort out the specific processor (CPU) products released by AMD and their detailed information during the decade since Lisa Su became the CEO of AMD to 2023(include 2023). If individual information cannot be retrieved from the Internet, do not make up, just output \"NA\" in the corresponding cell. By the way, the core architechture of processor(CPU) should be Zen. And the core frequency uses the base clock.\n\nPlease output the sorted data in the format of one Markdown table. The column names in the table are: Time, Product Series, Processor Model, Core Architecture, Manufacturing Process (nm), Cores, Threads, Core Frequency (GHz), L2 Cache (MB), L3 Cache (MB), Graphics Model, Number of Graphics Cores.\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is:\n```markdown\n{data_content}\n```.\n\nEveryone says \"Su Ma\" saved AMD. Please help me compile a list of specific processor (CPU) products AMD has released since Lisa Su became CEO and launched the \"Zen\" architecture until 2024 (inclusive). If you can't find any information online, don't try to guess; just enter \"NA\" in the corresponding cell.","evaluation":"{\"unique_columns\": [\"processormodel\"], \"required\": [\"time\", \"productseries\", \"processormodel\", \"corearchitecture\", \"manufacturingprocess(nm)\", \"cores\", \"threads\", \"corefrequency(ghz)\", \"l2cache(mb)\", \"l3cache(mb)\", \"graphicsmodel\", \"numberofgraphicscores\"], \"eval_pipeline\": {\"processormodel\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"cores\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"threads\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"corefrequency(ghz)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"l2cache(mb)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"l3cache(mb)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"numberofgraphicscores\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"time\": {\"preprocess\": [\"norm_date\"], \"metric\": [\"date_near\"]}, \"productseries\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"corearchitecture\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"manufacturingprocess(nm)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"graphicsmodel\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
 {"instance_id":"ws_en_092","query":"I would like to know about the boy groups and girl groups (no sub-units) that debuted after 2000 (excluding 2000) and before 2025 (including 2025) from the three major entertainment companies in Korea (SM, JYP and YG) and the date and song when they first won the first place in music programs aired by the major radio stations (KBS, MBC, SBS and Mnet). Please provide the date when they first won a music program in the format yyyy-mm-dd, e.g. 2024-01-23. If the corresponding data is not retrieved, please fill in with \"-\".\n\nPlease output the organized data in one Markdown table format.\nThe column headers should be as follows:\nCompany Name, Debut Year, Official Group Name (English), First Win Date, First Win Song\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is:\n```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"officialgroupname(english)\"], \"required\": [\"companyname\", \"debutyear\", \"officialgroupname(english)\", \"firstwinsong\", \"firstwindate\"], \"eval_pipeline\": {\"companyname\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"debutyear\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"officialgroupname(english)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"firstwindate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"firstwinsong\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
-{"instance_id":"ws_en_093","query":"I want to catch up with the current large language model trends, so I need to quickly learn about them. Please help me compile a list of LLM-related papers officially published by Doubao Seed Team from Bytedance and DeepSeek from 2023 to the first half of 2025. The list should include: Publication date (in yyyy-mm-dd format, e.g. 2024-02-01), Paper title (in original English as published), and authors.\n\nPlease output the compiled data in Markdown table format.\n The column headers should be:\nCompany Name, Publication Date, Paper Title, Authors.\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is:\n```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"papertitle\"], \"required\": [\"companyname\", \"publicationdate\", \"papertitle\", \"authors\"], \"eval_pipeline\": {\"publicationdate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"papertitle\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"companyname\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"authors\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
+{"instance_id":"ws_en_093","query":"I want to catch up with the current large language model trends, so I need to quickly learn about them. Please help me compile a list of LLM-related papers officially published by Doubao Seed Team from Bytedance and DeepSeek from 2023 to the first half of 2025. The list should include: Publication date (in yyyy-mm-dd format, e.g. 2024-02-01), Paper title (in original English as published), and authors. By the way, I only want papers published independently by Doubao Seed Team from Bytedance and the DeepSeek team, not collaborative papers.\n\nPlease output the compiled data in Markdown table format.\n The column headers should be:\nCompany Name, Publication Date, Paper Title, Authors.\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is:\n```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"papertitle\"], \"required\": [\"companyname\", \"publicationdate\", \"papertitle\", \"authors\"], \"eval_pipeline\": {\"publicationdate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"papertitle\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"companyname\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"authors\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
 {"instance_id":"ws_en_094","query":"I'm compiling a list of the top three box office films released in South Korea for each year from Jan 2010 to Dec 2024. Please organize the top three films each year based on cumulative box office earnings (in this context, \"box office\" refers to total cumulative gross revenue).\nFor each film, provide the release year, title, genre, director, lead actor\/actress, total box office revenue (in billions of KRW, rounded to the nearest integer), and total number of admissions.\n\nPlease output the organized data in Markdown table format with the following column headers:\n  Release Year\n  Title\n  Genre\n  Director\n  Lead Actor\/Actress\n  Total Box Office (billions of KRW)\n  Total Admissions\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is:\n```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"title\"], \"required\": [\"releaseyear\", \"title\", \"genre\", \"director\", \"leadactor\/actress\", \"totalboxoffice(billionsofkrw)\", \"totaladmissions\"], \"eval_pipeline\": {\"releaseyear\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"title\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"totalboxoffice(billionsofkrw)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"totaladmissions\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"genre\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"director\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"leadactor\/actress\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
-{"instance_id":"ws_en_095","query":"Please help me compile a list of Korean dramas that were first released on Netflix from Jan 2022 to Dec 2024. I need the following information for each title: Drama Title, Release Date (YYYY-MM-DD), Director, Number of Episodes and Awards (if the drama or related cast and staff have not received any awards, use “\/” to indicate this). By the way, I only need the awarded prizes, not the ones that were only nominated.\n\nPlease output the organized data in Markdown table format.\n The column headers should be as follows:\nDrama Title, Release Date, Director, Number of Episodes, Awards.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is: \n ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"dramatitle\"], \"required\": [\"dramatitle\", \"releasedate\", \"director\", \"numberofepisodes\", \"awards\"], \"eval_pipeline\": {\"releasedate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"numberofepisodes\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"dramatitle\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"director\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"awards\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
+{"instance_id":"ws_en_095","query":"Please help me count the Korean dramas that aired on Netflix between January 2024 and December 2024. I need the Drama Title, Premiere Date(YYYY-MM-DD), Director, Number of Episodes and Awards from Baeksang Art Awards and Blue Dragon Film Awards won by the drama, its director. If the drama or related cast and staff have not received any awards, use “\/” to indicate this. By the way, I only need the awarded prizes, not the ones that were only nominated.\n\nPlease output the organized data in Markdown table format.\n The column headers should be as follows:\nDrama Title, Premiere Date, Director, Number of Episodes, Awards.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is: \n ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"dramatitle\"], \"required\": [\"dramatitle\", \"releasedate\", \"director\", \"numberofepisodes\", \"awards\"], \"eval_pipeline\": {\"releasedate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"numberofepisodes\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"dramatitle\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"director\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"awards\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
 {"instance_id":"ws_en_096","query":"I want to buy a camera to take landscape photos. I heard that Nikon is good at taking landscapes and want to buy a mirrorless camera. Please help me collect the information of all Nikon Z series products as of the first half of 2025. The information I want to know is: effective pixels, weight, maximum frames per second, shutter speed range, highest number of focus points, digital image processor, ISO range, in-camera shock reduction technology.\nThe weight specifically refers to the weight of the camera body itself, including the battery and memory card; The highest number of focus points refers to the higher value between single-point AF and auto-area AF.\n\nPlease output the sorted data in the format of one Markdown table. The column names in the table are: Camera Model, Effective Pixels, Weight, Maximum Frames per Second, Shutter Speed Range, Highest Number of Focus Points, Digital Image Processor, ISO Range, In-camera Vibration Reduction Technology.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is: \n ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"cameramodel\"], \"required\": [\"cameramodel\", \"effectivepixels\", \"weight\", \"maximumframespersecond\", \"shutterspeedrange\", \"highestnumberoffocuspoints\", \"digitalimageprocessor\", \"isorange\", \"in-cameravibrationreductiontechnology\"], \"eval_pipeline\": {\"cameramodel\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"maximumframespersecond\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"weight\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"shutterspeedrange\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"digitalimageprocessor\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"isorange\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"in-cameravibrationreductiontechnology\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"effectivepixels\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"highestnumberoffocuspoints\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
-{"instance_id":"ws_en_097","query":"Tony Leung is a well-known film actor. Please help me sort out all the movies in which he has played the leading role and that have been released since his debut. The time span is 1985-2024 (including 1985 and 2024).\nThe sorted information should include: film title, director, leading actress, year of premiere, distributor, and awards (only include the awards he won rather than any awards he was nominated for).\n\nPlease output the sorted data in the format of one Markdown table. The column names in the table are as follows: Movie Title, Director, Leading Actress, Year of Premiere, Distributor, Awards.\nIf some information cannot be retrieved, please output \"-\".\nAwards only refer to the awards won by Tony Leung in this film.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is: \n ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"movietitle\"], \"required\": [\"movietitle\", \"director\", \"leadingactress\", \"yearofpremiere\", \"distributor\", \"awards\"], \"eval_pipeline\": {\"yearofpremiere\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"movietitle\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"awards\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"director\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"leadingactress\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"distributor\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
+{"instance_id":"ws_en_097","query":"Tony Leung is a well-known film actor. Please help me sort out all the movies in which he has acted in and that have been released since his debut. The time span is 2000-2023 (including 2000 and 2023).\nThe sorted information should include: film title, director, leading actress, year of premiere, distributor, and awards (only include the awards he won rather than any awards he was nominated for). By the way, please exclude anthology films, re-edited and re-released, and movies that he's dubbed.\n\nPlease output the sorted data in the format of one Markdown table. The column names in the table are as follows: Movie Title, Director, Leading Actress, Year of Premiere, Distributor, Awards.\nIf some information cannot be retrieved, please output \"-\".\nAwards only refer to the awards won by Tony Leung in this film.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is: \n ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"movietitle\"], \"required\": [\"movietitle\", \"director\", \"leadingactress\", \"yearofpremiere\", \"distributor\", \"awards\"], \"eval_pipeline\": {\"yearofpremiere\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"movietitle\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"awards\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"director\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"leadingactress\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"distributor\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
 {"instance_id":"ws_en_098","query":"I'm wondering about the recent development of Africa. Can you list the GDP annual growth rate of all the countries in Sub-Saharan Africa from 2022-2024 (inclusive),  citing trustworthy number from World Bank? If some information cannot be retrieved, please output \"NA\".\n\nPlease output the sorted data in the format of a single Markdown table. The column names in the table are as follows: \nCountry, 2024 GDP growth (%), 2023 GDP growth (%),  2022 GDP growth (%)\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"country\"], \"required\": [\"country\", \"2024gdpgrowth(%)\", \"2023gdpgrowth(%)\", \"2022gdpgrowth(%)\"], \"eval_pipeline\": {\"2024gdpgrowth(%)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2023gdpgrowth(%)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2022gdpgrowth(%)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"country\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
-{"instance_id":"ws_en_099","query":"Compile a comprehensive list of U.S. wildfires that caused no less than 10 deaths between 2000 and 2024 (including 2000 and 2024). I need information about:\nEvent \nPrimary State Affected (If there are multiple affected states, separate them by comma)\nDeath Toll\nAcres Burned\nNumber of Structures Destroyed\nStart Month (YYYY-MM)\nDamage (Billion USD)\n\nPlease output the organized data in Markdown table format.\nThe column names in the table should be, in order:\nEvent, Primary State Affected, Death Toll, Acres Burned, Structure Destroyed, Start Month, Damage(Billion)\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is: \n ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"startmonth\"], \"required\": [\"event\", \"primarystateaffected\", \"deathtoll\", \"acresburned\", \"structuredestroyed\", \"startmonth\", \"damage(billion)\"], \"eval_pipeline\": {\"startmonth\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"acresburned\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"structuredestroyed\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"damage(billion)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"deathtoll\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"event\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"primarystateaffected\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
-{"instance_id":"ws_en_100","query":"Help me to compile a complete list of U.S. Supreme Court justices who were successfully confirmed from President Richard Nixon’s first term through President Joe Biden’s term. For each justice, record:\nName, Position (Chief Justice or Associate Justice), Tenure Start Date(in the format as YYYY-MM-DD), Tenure End Date (enter “NA” for currently serving justices as of the end of 2024), Nominating President, President’s Party Affiliation, Ideological Leaning (Conservative or Liberal), Most Recent Prior Position\n\nPlease output the organized data in Markdown table format.\nThe column names in the table should be, in order:\nName | Position | Tenure Start Date | Tenure End Date | Nominating President | President’s Party | Ideological Leaning | Previous Position\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is: \n ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"name\", \"position\"], \"required\": [\"name\", \"position\", \"tenurestartdate\", \"tenureenddate\", \"nominatingpresident\", \"president’sparty\", \"ideologicalleaning\", \"previousposition\"], \"eval_pipeline\": {\"tenurestartdate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"tenureenddate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"ideologicalleaning\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"president’sparty\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"position\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"name\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"nominatingpresident\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"previousposition\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
-{"instance_id":"ws_zh_001","query":"我要做电影研究，需要你列出来2020年-2024年（包含2020年和2024年）每年���国、美国本国票房前五的电影，表头需要包括年份、国家（如中国、美国）、电影名、导演、本国整体累计票房收益（不局限于当年，以亿为单位，保留到小数点后一位，例如7.9亿元，需要带上各国货币单位，中国电影以亿元为单位，美国电影为亿美元为单位）、电影类型。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。请注意，对于当年12月末上映的电影、大部分票房收益落在下一年的，视为下一年的电影。\n表格中的列名依次为：\n年份、国家、电影名、导演、本国累计票房收益、电影类型。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"国家\", \"电影名\"], \"required\": [\"年份\", \"国家\", \"电影名\", \"导演\", \"本国累计票房收益\", \"电影类型\"], \"eval_pipeline\": {\"国家\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"本国累计票房收益\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"导演\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n答出子集且未答出参考答案以外的内容时可算正确\"}, \"电影类型\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n答出参考答案中的部分类型（即子集）即视为正确、基于权威来源及官方依据的类型标注同样正确、答出其中一个子集其他类型内容合理也视为正确。\"}, \"电影名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
-{"instance_id":"ws_zh_002","query":"小孩本科毕业想去香港读研究生，想读商学院，帮我整理下26年香港大学、香港中文大学、香港科技大学的相关具体专业（不要mba）、要求和学费（以万港元为单位），方便我们对比这看，弄成一个表格直接给我就可以。然后专业不要那种和外国学校合办的，或者那种双硕士的。中文输出。每个专业分行输出，每行专业对应的学校不得省略。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n学校名称、专业、学费、入学要求。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"学校名称\", \"专业\"], \"required\": [\"学校名称\", \"专业\", \"学费\", \"入学要求\"], \"eval_pipeline\": {\"学校名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"入学要求\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n需要覆盖参考答案的所有要求（学位学历、英语\/学习成绩、教育背景、工作经验、推荐信等），尤其是要求细节要一致。\"}, \"学费\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"专业\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
+{"instance_id":"ws_en_099","query":"Compile a comprehensive list of U.S. wildfires that caused no less than 10 deaths between 2000 and 2024 (including 2000 and 2024). I need information about:\nEvent \nPrimary State Affected (If there are multiple affected states, separate them by comma)\nDeath Toll\nAcres Burned\nNumber of Structures Destroyed\nStart Month (YYYY-MM)\nDamage (Billion USD)\nIf any information you can't retrive online, just output \"-\".\n\nPlease output the organized data in Markdown table format.\nThe column names in the table should be, in order:\nEvent, Primary State Affected, Death Toll, Acres Burned, Structure Destroyed, Start Month, Damage(Billion)\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is: \n ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"startmonth\"], \"required\": [\"event\", \"primarystateaffected\", \"deathtoll\", \"acresburned\", \"structuredestroyed\", \"startmonth\", \"damage(billion)\"], \"eval_pipeline\": {\"startmonth\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"acresburned\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"structuredestroyed\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"damage(billion)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"deathtoll\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"event\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"primarystateaffected\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
+{"instance_id":"ws_en_100","query":"Help me to compile a complete list of U.S. Supreme Court justices who were successfully confirmed from President Richard Nixon’s first term through President Joe Biden’s term. For each justice, record:\nName, Position (Chief Justice or Associate Justice), Tenure Start Date(in the format as YYYY-MM-DD), Tenure End Date (enter “NA” for currently serving justices as of the end of 2024), Nominating President, President’s Party Affiliation, Ideological Leaning (Conservative or Liberal), Most Recent Prior Position\n\nPlease output the organized data in Markdown table format.\nThe column names in the table should be, in order:\nName | Position | Tenure Start Date | Tenure End Date | Nominating President | President’s Party | Ideological Leaning | Previous Position\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is: \n ```markdown\n{data_content}\n```.","evaluation":"{\"unique_columns\": [\"name\", \"position\"], \"required\": [\"name\", \"position\", \"tenurestartdate\", \"tenureenddate\", \"nominatingpresident\", \"president’sparty\", \"ideologicalleaning\", \"previousposition\"], \"eval_pipeline\": {\"tenurestartdate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"tenureenddate\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"ideologicalleaning\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"position\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"name\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"nominatingpresident\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"previousposition\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}, \"president’sparty\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence.\"}}}","language":"en"}
+{"instance_id":"ws_zh_001","query":"我要做电影研究，需要你列出来2020年-2024年（包含2020年和2024年）每个自然年（calendar year）的中国、美国本国票房前五的电影，表头需要包括年份、国家（如中国、美国）、电影名、导演、本国整体累计票房收益（不局限于当年，以亿为单位，保留到小数点后一位，例如7.9亿元，需要带上各国货币单位，中国电影以亿元为单位，美国电影为亿美元为单位）、电影类型。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n年份、国家、电影名、导演、本国累计票房收益、电影类型。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"国家\", \"电影名\"], \"required\": [\"年份\", \"国家\", \"电影名\", \"导演\", \"本国累计票房收益\", \"电影类型\"], \"eval_pipeline\": {\"国家\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"本国累计票房收益\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"导演\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n答出子集且未答出参考答案以外的内容时可算正确\"}, \"电影类型\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n答出参考答案中的部分类型（即子集）即视为正确、基于权威来源及官方依据的类型标注同样正确、答出其中一个子集其他类型内容合理也视为正确。\"}, \"电影名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
+{"instance_id":"ws_zh_002","query":"小孩本科毕业想去香港读研究生，想读商学院，帮我整理下26年香港大学、香港中文大学、香港科技大学的相关具体专业（不要mba）、要求和学费（以万港元为单位），方便我们对比这看，弄成一个表格直接给我就可以。然后专业不要那种和外国学校合办的，或者那种双硕士的。中文输出。每个专业分行输出，每行专业对应的学校不得省略。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n学校名称、专业、学费、入学要求。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"学校名称\", \"专业\"], \"required\": [\"学校名称\", \"专业\", \"学费\", \"入学要求\"], \"eval_pipeline\": {\"学校名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"入学要求\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n需要覆盖参考答案主要英文资质要求，如参考答案包含推荐信以及特定技能（如会python）等特异化需求，也需要一并给出。\"}, \"学费\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"专业\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_003","query":"想报名2026年的研究生考试，帮我查一下中国2025年的A区211及以上院校，新闻与传播专业(专硕全日制)的复试分数线分别是多少（只看总分）。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\nA区地区、学校、2025年复试分数线。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"学校\"], \"required\": [\"a区地区\", \"学校\", \"2025年复试分数线\"], \"eval_pipeline\": {\"学校\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"a区地区\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"2025年复试分数线\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_004","query":"2019到2024年（包含2019年和2024年），福布斯全球富豪排行榜前10都是哪些人，盘点一下这些人的名字，排名，财富值，财富来源是啥，做成表给我。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略。\n表格中的列名依次为：\n年度、排名、姓名（中文）、姓名（英文）、财富值（亿美元）、财富来源。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"年度\", \"排名\"], \"required\": [\"年度\", \"排名\", \"姓名（中文）\", \"姓名（英文）\", \"财富值（亿美元）\", \"财富来源\"], \"eval_pipeline\": {\"年度\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"排名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"财富值（亿美元）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"姓名（中文）\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n 姓名(中文)列部分音译可能有差异\"}, \"姓名（英文）\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"财富来源\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_005","query":"在写美国政治的论文，给我罗列从美国建国到2025年的大选年份获胜的总统（给我中文名），表头还需要包括大选年份、总统党派、获胜的选举人民众得票数以及团票数，获取不到的数据用\/代替。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n年份、姓名、党派、民众票数、团票数。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"年份\", \"姓名\"], \"required\": [\"年份\", \"姓名\", \"党派\", \"民众票数\", \"团票数\"], \"eval_pipeline\": {\"年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"党派\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"团票数\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"民众票数\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"姓名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
@@ -111,7 +111,7 @@
 {"instance_id":"ws_zh_011","query":"2020年-2022年这三年，中国中央对地方的财政转移支付情况有什么变化，分地区整理一下整体支付转移、一般性支付转移和专项支付转移的数字，单位默认是亿元，注意以上数字均是决算数而非预算数，数值均保留两位小数。如果对应地区没有对应的数字，表格中用-代替即可。注意地区只统计省、自治区、直辖市。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n地区、2022年转移支付决算数、2022年一般性转移支付决算数、2022年专项转移支付决算数、2021年转移支付决算数、2021年一般性转移支付决算数、2021年专项转移支付决算数、2020年转移支付决算数、2020年一般性转移支付决算数、2020年专项转移支付决算数。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"地区\"], \"required\": [\"地区\", \"2022年转移支付决算数\", \"2022年一般性转移支付决算数\", \"2022年专项转移支付决算数\", \"2021年转移支付决算数\", \"2021年一般性转移支付决算数\", \"2021年专项转移支付决算数\", \"2020年转移支付决算数\", \"2020年一般性转移支付决算数\", \"2020年专项转移支付决算数\"], \"eval_pipeline\": {\"2022年转移支付决算数\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.01}, \"2022年一般性转移支付决算数\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.01}, \"2022年专项转移支付决算数\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.01}, \"2021年转移支付决算数\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.01}, \"2021年一般性转移支付决算数\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.01}, \"2021年专项转移支付决算数\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.01}, \"2020年转移支付决算数\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.01}, \"2020年一般性转移支付决算数\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.01}, \"2020年专项转移支付决算数\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.01}, \"地区\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字���应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_012","query":"整合《财富》给出的世界2024年世界500强企业中的中国公司名单，包括榜单排名、企业名称，注意企业名称需要同时给出中英文，例如国家电网有限公司（STATE GRID)、营业收入、利润，并指明这些中国企业的总部所在城市，请注意台湾的企业也需要统计。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n排名、企业名称、营业收入（百万美元）、利润（百万美元）、总部所在城市。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"企业名称\"], \"required\": [\"排名\", \"企业名称\", \"营业收入（百万美元）\", \"利润（百万美元）\", \"总部所在城市\"], \"eval_pipeline\": {\"排名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"营业收入（百万美元）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"利润（百万美元）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"总部所在城市\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"企业名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_013","query":"现在是2025年6月，我最近沉迷说唱，想知道格莱美奖最近30年都有哪些说唱类别的奖项，以及这些奖项的获奖歌曲\/专辑的演唱者都是谁，奖项名称、获奖歌曲\/专辑、演唱者都不需要翻译成中文，假如演唱者有feat也要一并纳入，例如 Killer Mike (ft. André 3000, Future & Eryn Allen Kane)，以官方给出的演唱者顺序为准。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n届数、奖项名称、获奖歌曲\/专辑、演唱者。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"届数\", \"奖项名称\"], \"required\": [\"届数\", \"奖项名称\", \"获奖歌曲\/专辑\", \"演唱者\"], \"eval_pipeline\": {\"奖项名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"届数\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"获奖歌曲\/专辑\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"演唱者\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
-{"instance_id":"ws_zh_014","query":"帮我把邓紫棋从2010年1月1日-2025年5月1日期间举行的所有巡回演唱会的具体日期（xx年xx月xx日格式，不要用区间表示）、演唱会中文名、演唱会英文名、举办国家、举办城市、举办场馆列出来，每一场次为一行，按照日期从小到大排序。举办地是港澳台则举办国家输出中国。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略。\n表格中的列名依次为：具体日期、演唱会中文名、演唱会英文名、举办国家、举办城市、举办场馆。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"具体日期\"], \"required\": [\"具体日期\", \"演唱会中文名\", \"演唱会英文名\", \"举办国家\", \"举办城市\", \"举办场馆\"], \"eval_pipeline\": {\"具体日期\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"举办国家\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"演唱会中文名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n演唱会中文名存在live也可算正确\"}, \"演唱会英文名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"举办城市\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"举办场馆\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
+{"instance_id":"ws_zh_014","query":"帮我把邓紫棋从2010年1月1日-2025年5月1日期间举行的所有巡回演唱会的具体日期（xx年xx月xx日格式，不要用区间表示）、演唱会中文名、演唱会英文名、举办国家、举办城市、举办场馆列出来，每一场次为一行，按照日期从小到大排序。举办地是港澳台则举办国家输出中国。举办国家写国家简称即可，例如中国、美国、英国、澳大利亚、马来西亚、新加坡。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略。\n表格中的列名依次为：具体日期、演唱会中文名、演唱会英文名、举办国家、举办城市、举办场馆。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"具体日期\"], \"required\": [\"具体日期\", \"演唱会中文名\", \"演唱会英文名\", \"举办国家\", \"举办城市\", \"举办场馆\"], \"eval_pipeline\": {\"具体日期\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"举办国家\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"演唱会中文名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n演唱会中文名存在live也可算正确\"}, \"演唱会英文名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"举办城市\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"举办场馆\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_015","query":"想了解一下截止到2025年5月（包含5月），神舟系列载人航天任务的记录，包含任务名称、航天员姓名、发射地点、发射时间（精确到秒）、着陆时间、着陆地点、任务时长，并且以表格的形式汇总，中文输出。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n任务名称、航天员姓名、发射地点、发射时间、着陆时间、着陆地点、任务时长。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"任务名称\"], \"required\": [\"任务名称\", \"航天员姓名\", \"发射地点\", \"发射时间\", \"着陆时间\", \"着陆地点\", \"任务时长\"], \"eval_pipeline\": {\"任务名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"航天员姓名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"发射时间\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"着陆时间\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"任务时长\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n任务时长误差在一分钟内都算对。\"}, \"着陆地点\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"发射地点\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_016","query":"统计下2022-2024年期间（包含2022年和2024年）中国评定的5A级景区有哪些，需包含：景区名称、所属地区、评定年份、门票价格（成人旺季门票）。请以一整个Markdown表格的格���输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n景区名称、所属地区、评定年份、门票价格。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"景区名称\"], \"required\": [\"景区名称\", \"所属地区\", \"评定年份\", \"门票价格\"], \"eval_pipeline\": {\"景区名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"门票价格\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"所属地区\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"评定年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_017","query":"我想查下，2022年5月到2025年5月（包含2022年5月和2025年5月），全国铁路运输的一些重要指标，按照月份的维度进行统计，不包含香港、澳门、台湾省的数据。重要指标包含：时间、全国旅客总发送量\/万人、全国旅客总发送量相比上年同期增长%、全国旅客总周转量\/亿人公里、全国旅客总周转量相比上年同期增长%、全国货运总发送量\/万吨、全国货运总发送量相比上年同期增长%、全国货运总周转量\/亿吨公里、全国货运总周转量相比上年同期增长%。时间按照yyyy年m月，例如：2023年5月;同期增长%保留一位小数，例如：3.0。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：时间、全国旅客总发送量\/万人、全国旅客总发送量相比上年同期增长%、全国旅客总周转量\/亿人公里、全国旅客总周转量相比上年同期增长%、全国货运总发送量\/万吨、全国货运总发送量相比上年同期增长%、全国货运总周转量\/亿吨公里、全国货运总周转量相比上年同期增长%。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"时间\"], \"required\": [\"时间\", \"全国旅客总发送量\/万人\", \"全国旅客总发送量相比上年同期增长%\", \"全国旅客总周转量\/亿人公里\", \"全国旅客总周转量相比上年同期增长%\", \"全国货运总发送量\/万吨\", \"全国货运总发送量相比上年同期增长%\", \"全国货运总周转量\/亿吨公里\", \"全国货运总周转量相比上年同期增长%\"], \"eval_pipeline\": {\"时间\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"全国旅客总发送量\/万人\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"全国旅客总发送量相比上年同期增长%\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"全国旅客总周转量\/亿人公里\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"全国旅客总周转量相比上年同期增长%\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"全国货运总发送量\/万吨\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"全国货运总发送量相比上年同期增长%\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"全国货运总周转量\/亿吨公里\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"全国货运总周转量相比上年同期增长%\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}}}","language":"zh"}
@@ -124,10 +124,10 @@
 {"instance_id":"ws_zh_024","query":"想要了解下获得澳门政府获授博彩经营批给合同的公司在2024年的在澳经营情况，主要想了解下各公司的博彩净收益、其他非博彩收益(除博彩收益以外的其他收益的总和)，以及各公司所属五星级豪华酒店的房间数、入住率、平均房价、可入住客房收益。博彩净收益、非博彩收益以百万为单位，保留到小数点后两位；金额请采用港元做单位；若有少量数据无法获取，对应表格位置应该标记为“-”。请从官方渠道进行数据获取，若没有博彩净收益则提取娱乐场所收益当作博彩��益。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n年份\n博彩企业\n博彩净收益（百万）\n其他非博彩收益（百万）\n豪华五星级酒店\n房间数（个）\n入住率\n平均房租\n可入住客房收益。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"博彩企业\", \"豪华五星级酒店\"], \"required\": [\"年份\", \"博彩企业\", \"博彩净收益（百万）\", \"其他非博彩收益（百万）\", \"豪华五星级酒店\", \"房间数（个）\", \"入住率\", \"平均房租\", \"可入住客房收益\"], \"eval_pipeline\": {\"博彩净收益（百万）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"其他非博彩收益（百万）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"房间数（个）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"入住率\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"平均房租\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"可入住客房收益\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"博彩企业\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"豪华五星级酒店\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_025","query":"帮我统计2015年1月到2025年5月已发布的Redmi K系列和Note系列手机的全部基础机型，你需要分列显示出：手机型号、发布日期、出厂搭载系统、手机处理器品牌、手机cpu型号、cpu制造工艺、cpu核心数、屏幕尺寸、分辨率、电池容量、运行内存大小、存储空间大小、最高像素、发布时的售价。请将结果整理成一个markdown表格。\n注意事项：\n1.只统计国内的，不包含海外市场发布的手机机型\n2.基础机型指机型名称中不带后缀且在该机型版本中价格最低，例如小米13，8GB+128GB。常见后缀包含（pro、Turbo、ultra、A、S、R、E、I、xx版等），5G、4G 不算后缀\n3.若某款基础机型同时存在4G和5G版本，则该款基础机型只抓取5G版本的机型\n4.发布日期按照yyyy-mm-dd的格式；分辨率输出格式为 数字x数字，例如 1920x1080\n5.cpu制造工艺的输出格式为数字+单位；cpu核心数输出格式为x+核，例如6核\n6.最高像素只输出手机摄像头中像素最高的即可，输出格式为数字+单位\n7.手机cpu型号不要带品牌。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n手机型号、发布日期、出厂搭载系统、手机处理器品牌、手机cpu型号、cpu制造工艺、cpu核心数、屏幕尺寸、分辨率、电池容量、运行内存大小、存储空间大小、最高像素、发布时的售价。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"发布日期\"], \"required\": [\"手机型号\", \"发布日期\", \"出厂搭载系统\", \"手机处理器品牌\", \"手机cpu型号\", \"cpu制造工艺\", \"cpu核心数\", \"屏幕尺寸\", \"分辨率\", \"电池容量\", \"运行内存大小\", \"存储空间大小\", \"最高像素\", \"发布时的售价\"], \"eval_pipeline\": {\"发布日期\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"cpu制造工艺\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"cpu核心数\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"运行内存大小\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"存储空间大小\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"发布时的售价\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"手机型号\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n手机型号多答出5G或少答出5G都不扣分\"}, \"出厂搭载系统\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n小米澎湃输出为Xiaomi Hyper也可接受\"}, \"手机处理器品牌\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"手机cpu型号\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"屏幕尺寸\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"电池容量\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"分辨率\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"最高像素\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_026","query":"孩子要申请2025秋季的学校，但是专业一直定不下来，给我找找软科2024年发布的世界一流学科中，每个学科排名前三的学校都是哪些吧，再看看这些学校2025年在QS能排到多少名，学校名称需要给出学校英文官方全称为准。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略。如果找不到排名，请输出“未上榜”。\n表格中的列名依次为：\n软科世界一流学科、软科学科2024排名、学校名称、QS学校2025排名。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"软科世界一流学科\", \"软科学科2024排名\", \"学校名称\"], \"required\": [\"软科世界一流学科\", \"软科学科2024排名\", \"学校名称\", \"qs学校2025排名\"], \"eval_pipeline\": {\"软科世界一流学科\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"软科学科2024排名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"学校名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"qs学校2025排名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"zh"}
-{"instance_id":"ws_zh_027","query":"我需要2025年1月至5月（包含1月和5月）中国各已上市期货交易所（包括上海期货交易所、大连商品交易所、郑州商品交易所和广州期货交易所）每月的数据。\n对于上海期货交易所的数据，请确保其已包含上海国际能源交易中心（INE）的数据。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：交易所名称、统计月份、总成交量（手）、总成交额（万元）、总持仓量（手）。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"交易所名称\", \"统计月份\"], \"required\": [\"交易所名称\", \"统计月份\", \"总成交量（手）\", \"总成交额（万元）\", \"总持仓量（手）\"], \"eval_pipeline\": {\"交易所名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"总成交量（手）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"总成交额（万元）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"总持仓量（手）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"统计月份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
+{"instance_id":"ws_zh_027","query":"我需要2025年1月至5月（包含1月和5月）中国各已上市期货交易所（包括上海期货交易所、大连商品交易所、郑州商品交易所、广州期货交易所和中国金融期货交易所）每月的数据。\n对于上海期货交易所的数据，请确保其已包含上海国际能源交易中心（INE）的数据。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：交易所名称、统计月��、总成交量（手）、总成交额（万元）、总持仓量（手）。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"交易所名称\", \"统计月份\"], \"required\": [\"交易所名称\", \"统计月份\", \"总成交量（手）\", \"总成交额（万元）\", \"总持仓量（手）\"], \"eval_pipeline\": {\"交易所名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"总成交量（手）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"总成交额（万元）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"总持仓量（手）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"统计月份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_028","query":"我喜欢自然资源类的风景，马上国庆节准备出去玩，顺便做一份旅游研究，请你帮我整理搜狐官方网站上2023年度国内人气口碑景区-自然亲水类Top50的景点，港澳台不用考虑，做成表格给我，表头需要包含景点名称、所在省份、所在的省份在2023年的旅游综合收入以及接待人次。所在省份输出四川、湖南等即可，综合收入四舍五入以亿为单位，如20亿，游接待人次同样以亿为单位，精确到小数点后一位，例如2.8亿。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n景区名称、所在省份、所在省份旅游接待人次（亿）、所在的省份的旅游综合收入（亿）。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"景区名称\"], \"required\": [\"景区名称\", \"所在省份\", \"所在省份旅游接待人次（亿）\", \"所在的省份的旅游综合收入（亿）\"], \"eval_pipeline\": {\"景区名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"所在省份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"所在省份旅游接待人次（亿）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"所在的省份的旅游综合收入（亿）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}}}","language":"zh"}
 {"instance_id":"ws_zh_029","query":"请提供一份《中国新闻出版广电报》公布的2024年度优秀畅销书排行榜总榜前50名的表格，需包含书名、作者（如果有多个作者，之间用顿号分割，如果有外国作者，需要同时列出中文名和外文名）、出版时间（按照yyyy-mm的格式，例如2024-01，只需给到月即可）三项信息。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n书名、作者、出版时间。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"书名\"], \"required\": [\"书名\", \"作者\", \"出版时间\"], \"eval_pipeline\": {\"出版时间\": {\"preprocess\": [\"norm_date\"], \"metric\": [\"date_near\"]}, \"书名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"作者\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
-{"instance_id":"ws_zh_030","query":"我真的超爱周杰伦，帮我找出2004年1月到2010年9月（包含2024年1月和2010年9月）周杰伦发行的全部歌曲，需包含歌曲信息：歌曲名称、填词人、编曲人、发行时间、所属专辑、歌曲时长\n注意：\n1.我想要周杰伦原唱的，多人合唱也行，且不包含纯音乐。\n2.发行时间按照yyyy\/mm\/dd；歌曲时长按照x分x秒，例如3分5秒\n3.只需要找出在中国发行的\n4.不要现场版和demo版的歌曲\n5.只需要所属专辑是周杰伦的歌曲。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n歌曲名称、填词人、编曲人、发行时间、所属专辑、歌曲时长。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"歌曲名称\"], \"required\": [\"歌曲名称\", \"填词人\", \"编曲人\", \"发行时间\", \"所属专辑\", \"歌曲时长\"], \"eval_pipeline\": {\"发行时间\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"歌曲名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"所属专辑\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"填词人\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"编曲人\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"歌曲时长\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n歌曲时长误差在5秒内均算正确\"}}}","language":"zh"}
+{"instance_id":"ws_zh_030","query":"我真的超爱周杰伦，帮我找出2004年1月到2010年9月（包含2024年1月和2010年9月）周杰伦发行的全部歌曲，需包含歌曲信息：歌曲名称、填词人、编曲人、发行时间、所属专辑、歌曲时长\n注意：\n1.我想要周杰伦原唱的，多人合唱也行，且不包含纯音乐。\n2.发行时间按照yyyy\/mm\/dd；歌曲时长按照x分x秒，例如3分5秒\n3.只需要找出在中国发行的\n4.不要现场版和demo版的歌曲\n5.只需要收录到周杰伦录音室专辑的歌曲，不用包含仅通过单曲或单曲专辑、电影原声带、电视原声带、EP等形式发布的歌曲\n请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n歌曲名称、填词人、编曲人、发行时间、所属专辑、歌曲时长。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"歌曲名称\"], \"required\": [\"歌曲名称\", \"填词人\", \"编曲人\", \"发行时间\", \"所属专辑\", \"歌曲时长\"], \"eval_pipeline\": {\"发行时间\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"歌曲名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"所属专辑\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"填词人\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"编曲人\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"歌曲时长\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n歌曲时长误差在5秒内均算正确\"}}}","language":"zh"}
 {"instance_id":"ws_zh_031","query":"我需要你帮我分别统计2024年巴黎奥运会、2020年东京奥运会、2016年里约奥运会中全部中美洲和南美洲国家代表团所获得的金牌数量以及是哪些项目获得了金牌\n\n输出字段以及解释：\n奥运会名称：从：2024年巴黎奥运会、2020年东京奥运会、2016年里约奥运会三个中选择输出，不要改变名称\n国家：获得金牌的国家\n金牌数量：在某届奥运会中该国家获得金牌的总数\n夺冠项目：夺冠项目只用输出每个项目的大项即可，若一个大项夺得了多个金牌则输出：大项名称（该项目金牌数目）如：足球（2），如果该项目只有一枚金牌，则只输出大项名称即可，若某一届奥运会某个国家有多个夺冠项目，则这些项目合并在一个单元格中进行输出即可，每个项目用、隔开，中文输出。请将整理后的数据输出到一个Markdown格式的表格中。\n表格中的列名依次为：\n奥运会名称、国家、金牌数量、夺冠项目。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"奥运会名称\", \"国家\"], \"required\": [\"奥运会名称\", \"国家\", \"金牌数量\", \"夺冠项目\"], \"eval_pipeline\": {\"奥运会名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"国家\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"金牌数量\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"夺冠项目\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_032","query":"帮我整合一下2024年1月至2024年6月每个月中国华东和西南地区旅客吞吐量前20的机场\n\n输出字段以及解释：\n年月字段为统计的月份，输出格式为：2024年X月\n地区字段为统计的地区，只能输出华东或者西南\n机场名字段为统计的机场的名字\n排名字段为当前机场在当前月份和当前地区旅客吞吐量在前20中的排名（游客吞吐量越高排名越靠前）\n旅客吞吐量当前月份当前机场的旅客吞吐量，单位为人数请将整理后的数据输出到一个Markdown格式的表格中。\n表格中的列名依次为：\n年月、地区、机场名、排名、旅客吞吐量。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"年月\", \"排名\", \"地区\"], \"required\": [\"年月\", \"地区\", \"机场名\", \"排名\", \"旅客吞吐量\"], \"eval_pipeline\": {\"年月\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"排名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"地区\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"旅客吞吐量\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"机场名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n机场名可能会带上城市如成都天府，也可以算对\"}}}","language":"zh"}
 {"instance_id":"ws_zh_033","query":"想找一点好玩的动作游戏，帮我检索一下2024年steam年度最畅销游戏铂金榜上的游戏以及其发行公司，这些游戏公司发行过哪些游戏获得了2014——2024十年来TGA(The Game Awards)最佳动作游戏和最佳动作\/冒险游戏的奖项（提名的不算）。没有查询到获奖游戏时返回\"无\"\n\n需要输出的字段以及其含义：\n2024年steam年度最畅销游戏：该字段输出2024年steam年度最畅销游戏的名称\n发行公司：该字段指的是2024年steam年度最畅销游戏的发行公司\n获奖游戏：指的是对应的发行公司在2014——2024十年来获得TGA最佳动作游戏和最佳动作\/冒险游戏奖项的游戏（如果只是提名则不计入），若一个发行公司有多个游戏获奖，则全部输出到一个单元格，每个游戏之间用、隔开，中文输出。请将整理后的数据输出到一个Markdown格式的表格中。\n表格中的列名依次为：\n2024年steam年度最畅销游戏、发行公司、获奖游戏。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"2024年steam年度最畅销游戏\"], \"required\": [\"2024年steam年度最畅销游戏\", \"发行公司\", \"获奖游戏\"], \"eval_pipeline\": {\"2024年steam年度最畅销游戏\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"发行公司\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"获奖游戏\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
@@ -136,64 +136,64 @@
 {"instance_id":"ws_zh_036","query":"以表格的形式给我整理一份足球欧洲五大联赛从2020-21赛季至2024-25赛季的所有冠军球队、该赛季sofascore评分中的MVP球员及其当时所属俱乐部和评分分数的信息，中文输出。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：赛季、所属联赛、冠军俱乐部名称、MVP球员、所属俱乐部、评分。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"赛季\", \"所属联赛\"], \"required\": [\"赛季\", \"所属联赛\", \"冠军俱乐部名称\", \"mvp球员\", \"所属俱乐部\", \"评分\"], \"eval_pipeline\": {\"所属联赛\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"评分\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"赛季\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"冠军俱乐部名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"所属俱乐部\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"mvp球员\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_037","query":"最近小区需要更换物业公司，都说中指云专门做房地产报告的，我想了解一下它发布的2025年4月微信公众号上品牌传播TOP50的物业公司，包括排名、物业公司名称、微信公众号名称和阅读量，阅读量高的感觉肯定住户多靠谱点。对了我还希望是老牌公司，给我把这些物业公司的成立年份也整理出来吧，表格形式就行。年份需要带单位例如：2005年。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n排名、物业公司名称、微信公众号名称、阅读量、物业公司成立年份。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"排名\"], \"required\": [\"排名\", \"物业公司名称\", \"微信公众号名称\", \"阅读量\", \"物业公司成立年份\"], \"eval_pipeline\": {\"排名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"物业公司名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"物业公司成立年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"阅读量\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"微信公众号名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_038","query":"我在研究旅游度假区相关知识，给我一份截止到2025年完整的中国国家级旅游度假区统计表，并举出各个度假区所在省份以及认定年份的信息表，其中认定年份需要输出数字+年份，如2010年，即可。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n景区名称、省份、认定年份。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"景区名称\"], \"required\": [\"景区名称\", \"省份\", \"认定年份\"], \"eval_pipeline\": {\"景区名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"认定年份\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.0}, \"省份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
-{"instance_id":"ws_zh_039","query":"我要写一篇关于旅游饭店的论文，现在需要整理截止2025年7月（包含2025年7月）北京的五星级饭店相关信息，包括星级饭店名称、所在区、星级标牌号，以及具体地址，请以官方认定的五星级饭店为准。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：饭店名称、市区、星级标牌号、具体地址。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"饭店名称\"], \"required\": [\"饭店名称\", \"市区\", \"星级标牌号\", \"具体地址\"], \"eval_pipeline\": {\"饭店名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"市区\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"星级标牌号\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"具体地址\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n缺失北京市时可豁免\"}}}","language":"zh"}
+{"instance_id":"ws_zh_039","query":"我要写一篇关于旅游饭店的论文���现在需要整理截止2025年7月（包含2025年7月）北京的五星级饭店相关信息，包括星级饭店名称、所在区、星级标牌号，以及具体地址，请以国家旅游局发布的“五星级旅游酒店”名单为准。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：饭店名称、市区、星级标牌号、具体地址。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"饭店名称\"], \"required\": [\"饭店名称\", \"市区\", \"星级标牌号\", \"具体地址\"], \"eval_pipeline\": {\"饭店名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"市区\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"星级标牌号\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"具体地址\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n缺失北京市时可豁免\"}}}","language":"zh"}
 {"instance_id":"ws_zh_040","query":"整理下2010-2024年（包含2010年和2024年）美国发生的校园内枪击案，需要整理的信息包括案件发生日期（当地时间）、所在州、发生场所(学校)、凶手姓名、凶手身份、凶手年龄(行凶时)、凶手结局、死亡人数。（统计的案件要求枪击案的死亡人数大于0，且不包括凶手死亡。）凶手姓名、凶手身份、凶手年龄(行凶时)、凶手结局没有查询到内容时返回\"尚未提供\"；若凶手存在多名的时候姓名、年龄、身份等用顿号隔开输出。日期按照yyyy\/mm\/dd，例如：2015\/03\/30。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n案件发生日期、所在州、发生场所(学校)、凶手姓名、凶手身份、凶手年龄(行凶时)、凶手结局、死亡人数。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"案件发生日期\", \"发生场所(学校)\"], \"required\": [\"案件发生日期\", \"所在州\", \"发生场所(学校)\", \"凶手姓名\", \"凶手身份\", \"凶手年龄(行凶时)\", \"凶手结局\", \"死亡人数\"], \"eval_pipeline\": {\"案件发生日期\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"死亡人数\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"发生场所(学校)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"凶手姓名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"凶手身份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"凶手年龄(行凶时)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"凶手结局\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"所在州\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n英译中存在翻译差异\"}}}","language":"zh"}
 {"instance_id":"ws_zh_041","query":"我需要你帮我搜集2025年上半年（1月到6月），四川省内法院和检察院的聘用制书记员的所有招考单位和相关信息，。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文，列名依次为招聘单位、招聘岗位、招聘人数、报考年龄、进面比例、报名时间。未查询到的内容返回\"\/\"。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n招聘单位、招聘岗位、招聘人数、报考年龄、进面比例、报名时间。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"招聘单位\"], \"required\": [\"招聘单位\", \"��聘岗位\", \"招聘人数\", \"报考年龄\", \"进面比例\", \"报名时间\"], \"eval_pipeline\": {\"招聘人数\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"招聘单位\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"招聘岗位\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"报考年龄\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"进面比例\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n1：5和5:1都对\"}, \"报名时间\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_042","query":"帮我统计威尼斯国际电影节第76届金狮奖（2019）主竞赛单元中入围金狮奖角逐的的作品信息（包含1部获奖作品及若干提名作品），我想研究一下找找规律。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n作品名称、导演、类型、制片国家\/地区、片长。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```。片长给出威尼斯国际电影节放映版本的片长即可，格式为xx分钟。","evaluation":"{\"unique_columns\": [\"作品名称\"], \"required\": [\"作品名称\", \"导演\", \"类型\", \"制片国家\/地区\", \"片长\"], \"eval_pipeline\": {\"片长\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"作品名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"类型\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n类型答出子集且其他内容符合电影内容可算对\"}, \"制片国家\/地区\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n答出的全部内容属于答案的子集时可算对\"}, \"导演\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_043","query":"为了了解近期歌曲潮流，帮我整理出2025年第20期（2025年5月19日至2025年5月25日）腾讯音乐榜上的由你榜热度排名前15的榜单，包括榜单名称、排名、歌曲名称、演唱者、发行日期、歌曲时长、作词人、作曲人。注意一下格式，发行日期要是yyyy\/mm\/dd的形式，歌曲时长为x分y秒，多位作词人、多位作曲人之间用顿号。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：榜单名称、排名、歌曲名称、演唱者、发行日期、歌曲时长、作词人、作曲人。\n。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"排名\"], \"required\": [\"榜单名称\", \"排名\", \"歌曲名称\", \"演唱者\", \"发行日期\", \"歌曲时长\", \"作词人\", \"作曲人\"], \"eval_pipeline\": {\"榜单名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"排名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"发行日期\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"歌曲时长\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"演唱者\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"作词人\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"作曲人\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"歌曲名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
-{"instance_id":"ws_zh_044","query":"我想知道2025年5月1日-5月30日北京共举办了哪些展会（展会指展览会、博览会、家博会、产品展等集中展示产品、技术或服务的活动，不包含画展、摄影展、毕业展），只要展会开始日期符合要求的就算，帮我按照举办时期（xxxx年xx月xx日-xx月xx日）、展会全称、展馆名称、展馆详细地址（xx市xx区xx路\/街xx号）、主办单位、承办单位的列名梳理出来，按照开始日期从早到晚排序。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n举办时期、展会全称、展馆名称、展馆详细地址、主办单位、承办单位\n每一行整理一个展会，若一个展会的主办单位和承办单位有多个，用顿号隔开即可。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"展会全称\"], \"required\": [\"举办时期\", \"展会全称\", \"展馆名称\", \"展馆详细地址\", \"主办单位\", \"承办单位\"], \"eval_pipeline\": {\"展会全称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"举办时期\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"展馆名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"展馆详细地址\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"主办单位\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n主办单位数量不能多于参考答案，只给出答案的子集算对\"}, \"承办单位\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n承办单位只给出答案的子集算对\"}}}","language":"zh"}
+{"instance_id":"ws_zh_044","query":"我想知道2025年5月1日-5月30日北京共举办了哪些展会（展会指展览会、博览会、家博会、产品展等集中展示产品、技术或服务的活动，不包含画展、摄影展、毕业展、艺术展），只要展会开始日期符合要求的就算，帮我按照举办时期（xxxx年xx月xx日-xx月xx日）、展会全称、展馆名称、展馆详细地址（xx市xx区xx路\/街xx号）、主办单位、承办单位的列名梳理出来，按照开始日期从早到晚排序。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n举办时期、展会全称、展馆名称、展馆详细地址、主办单位、承办单位\n每一行整理一个展会，若一个展会的主办单位和承办单位有多个，用顿号隔开即可。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"展会全称\"], \"required\": [\"举办时期\", \"展会全称\", \"展馆名称\", \"展馆详细地址\", \"主办单位\", \"承办单位\"], \"eval_pipeline\": {\"展会全称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"举办时期\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"展馆名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"展馆详细地址\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"主办单位\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n主办单位数量不能多于参考答案，只给出答案的子集算对\"}, \"承办单位\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n承办单位只给出答案的子集算对\"}}}","language":"zh"}
 {"instance_id":"ws_zh_045","query":"帮我找下sm和yg在2015年及以后正式出道的女团有哪些，小分队这种不要，看看具体出道时间，还想了解下截止2025年3月，这些女团出道后发了哪些专辑，发行时间，每张专辑包含的具体歌曲。演唱会专辑或者单曲这种不要。时间格式按照xxxx年x月x日，例如2014年5月4日。请注意每个专辑分条输出，对应的女团名称需要对应输出，不得合并单元格或省略。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n娱乐公司、女团名、出道日期、专辑名、发行时间、包含歌曲。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"专辑名\"], \"required\": [\"娱乐公司\", \"女团名\", \"出道日期\", \"专辑名\", \"发行时间\", \"包含歌曲\"], \"eval_pipeline\": {\"出道日期\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"发行时间\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"娱乐公司\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"专辑名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"包含歌曲\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"女团名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_046","query":"我想了解一下中国民族自治地方的发展情况，帮我梳理下我国所有自治州（30个）的基本情况吧，以Markdown表格的格式整理成数据给我，包括自治州名称、设立时间、总面积、下辖地区（具体的市县名）、2024年州长姓名、州长所属民族。注意一下格式，成立日期用yyyy年mm月dd日的形式，总面积单位为平方公里。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n自治州名称、设立时间、总面积、下辖地区、2024年州长姓名、州长所属民族。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"自治州名称\"], \"required\": [\"自治州名称\", \"设立时间\", \"总面积\", \"下辖地区\", \"2024年州长姓名\", \"州长所属民族\"], \"eval_pipeline\": {\"自治州名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"2024年州长姓名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"州长所属民族\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"总面积\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"下辖地区\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"设立时间\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_047","query":"整理出截止至2025年6月“一带一路”倡议涉及合作的所有非洲国家及其首都，并根据世界银行（World Bank）的统计，查询这些国家的国土面积（单位为平方公里，使用2022年统计数据，保留整数）、人口密度（人 \/ 平方公里，使用2022年统计数据，保留整数）、总人口（单位为万，使用2023年统计数据，保留整数）、以及商品贸易占GDP的比重（单位为GDP的百分比，使用2023年统计数据，保留小数点后一位）。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n国家、首都、面积（平方公里）、人口密度（人\/平方公里）、总人口（万）、商品贸易（GDP的百分比）。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"国家\"], \"required\": [\"国家\", \"首都\", \"面积（平方公里）\", \"人口密度（人\/平方公里）\", \"总人口（万）\", \"商品贸易（gdp的百分比）\"], \"eval_pipeline\": {\"面积（平方公里）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"人口密度（人\/平方公里）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"总人口（万）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"商品贸易（gdp的百分比）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"国家\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"首都\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_048","query":"我打算做个盘点视频对比20年来85花的变化，帮我梳理下杨颖、景甜、刘诗诗、唐嫣、杨幂、刘亦菲、赵丽颖2004年、2014年、2024年参演开播的所有电视剧或者电影，除了影视名称，还需要找到她们在戏里的角色名、搭档演员（男一号演员名字），以及播放平台，如果先在电视播出的话写出首播频道即可，先在网络播出的话写出首播平台即可，院线电影无需填写播放平台用\/代替。注意没有出演仅配音的作品不算在内。播放平台没有查询到内容时返回\"\/\"。年份需要带单位例如：2004年。请注意每个电视剧或者电影分条输出，对应的明星需要对应输出，不得合并单元格或省略。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n演员名称、播出年份、影视名称、角色名、搭档演员、播放平台。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"演员名称\", \"影视名称\"], \"required\": [\"演员名称\", \"播出年份\", \"影视名称\", \"角色名\", \"搭档演员\", \"播放平台\"], \"eval_pipeline\": {\"演员名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"播出年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"播放平台\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n只答出子集可算对\"}, \"影视名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"角色名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n不加姓也行，如晴雪和风晴雪都可以\"}, \"搭档演员\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n中文名和英文名都可以\"}}}","language":"zh"}
 {"instance_id":"ws_zh_049","query":"我最近有些书荒，想买一些书来读一下，能不能按照排名给我整理一份2022-2024年（包含2022年和2024年）每年豆瓣阅读年度图书榜单总榜的前十名书籍，以及每年当当网的畅销书及好评排名前十的书籍，并给出这些书籍的作者名字？。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：年份、种类、排名、书籍、作者\n注意：种类中需要区分出豆瓣阅读年度图书、当当网畅销书、当当网好评书三种类型。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"年份\", \"种类\", \"排名\"], \"required\": [\"年份\", \"种类\", \"排名\", \"书籍\", \"作者\"], \"eval_pipeline\": {\"年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"种类\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"排名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"书籍\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"作者\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
-{"instance_id":"ws_zh_050","query":"我了解到billboard每年年末都会有一个单曲年终总榜，想知道2015-2024这十年的单曲年终top 10都是哪些歌曲？以及它们的演唱者都是谁？年份从2015往后排就行，歌曲和演唱者直接给英文就可以。年份不需要带单位，例如2017。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略。\n表格中的列名依次为：\n年份、排名、歌曲名称、歌手。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"年份\", \"排名\"], \"required\": [\"年份\", \"排名\", \"歌曲名称\", \"歌手\"], \"eval_pipeline\": {\"排名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"歌曲名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"歌手\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
+{"instance_id":"ws_zh_050","query":"我了解到billboard每年年末都会有一个单曲年终总榜，想知道2015-2024这十年（包含2015年和2024年）的单曲年终top 10都是哪些歌曲？以及它们的演唱者都是谁？歌曲和演唱者直接给英文就可以。年份不需要带单位，例如2017。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略。\n表格中的列名依次为：\n年份、排名、歌曲名称、歌手。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"年份\", \"排名\"], \"required\": [\"年份\", \"排名\", \"歌曲名称\", \"歌手\"], \"eval_pipeline\": {\"排名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"歌曲名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"歌手\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_051","query":"我是漫威电影的狂热粉丝，但是一直对漫威电影宇宙没有系统的整理过，把2025年4月25日之前上映的漫威电影宇宙中的电影给我汇总一下，包括电影名称（中文+英文）、中国大陆上映时间（格式为xxxx年x月x日，例如2000年3月24日）及中国大陆票房（单位：人民币）。如果中国大陆没有上映，上映时间为“未上映”，票房为“-”。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略。\n表格中的列名依次为：\n中文名称、英文名称、中国上映时间、中国大陆票房（单位：人民币）。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"中文名称\"], \"required\": [\"中文名称\", \"英文名称\", \"中国上映时间\", \"中国大陆票房（单位：人民币）\"], \"eval_pipeline\": {\"中国上映时间\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"中国大陆票房（单位：人民币）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"中文名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"英文名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
-{"instance_id":"ws_zh_052","query":"截止到2024年年底，给我整理一下北京不用指定就能直接刷医保治疗的定点A类医院，他们所在的区域（如朝阳区、海淀区）、具体地址、成立的时间（直接给出年份即可，例如1912年）和医疗等级（三级\/二级��，我看下哪儿看病方便。你需要将所有数据合并在一个表格里输出。注意：存在多个院区的医院需要根据院区分别进行输出，例如中国医学科学院北京协和医院东单院区需要和中国医学科学院北京协和医院西单院区分开输出，如果找不到对应的成立年份标记为NA。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n医院名称、区域、具体地址、成立年份、医疗等级。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"具体地址\"], \"required\": [\"医院名称\", \"区域\", \"具体地址\", \"成立年份\", \"医疗等级\"], \"eval_pipeline\": {\"成立年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"具体地址\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"医院名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"区域\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"医疗等级\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
+{"instance_id":"ws_zh_052","query":"截止到2024年年底，给我整理一下北京不用指定就能直接刷医保治疗的定点A类医院，他们所在的区域（如朝阳区、海淀区）、具体地址、成立的时间（直接给出年份即可，例如1912年）和医疗等级（三级\/二级），我看下哪儿看病方便。你需要将所有数据合并在一个表格里输出。注意：存在多个院区的医院需要根据院区分别进行输出，例如中国医学科学院北京协和医院东单院区需要和中国医学科学院北京协和医院西单院区分开输出，不同院区的成立时间需要单独查找，不可直接使用主院的成立时间。如果找不到对应的成立年份标记为NA。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n医院名称、区域、具体地址、成立年份、医疗等级。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"具体地址\"], \"required\": [\"医院名称\", \"区域\", \"具体地址\", \"成立年份\", \"医疗等级\"], \"eval_pipeline\": {\"成立年份\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.0}, \"具体地址\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"医院名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"区域\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"医疗等级\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_053","query":"在研究中国的过去人口变化趋势，请整理从改革开放以来的历次全国人口普查的如下数据 1）人口普查地区（按照不同的省、自治区、直辖市分行排列，不包含特别行政区和台湾省） 2）对应地区人口数量  3）对应地区的儿童数量（0-14岁）、青中年数量（15-59岁）、老年数量（60年以上）4）对应地区拥有或接受大学（指大专及以上）文化程度的人口数量\n请注意，统计数据以中国官网发布为准，找不到的数据在表格中以NA表述即可，涉及到人数时均精确到个位。你需要将所有数据合并在一个表格里输出。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n第x次人口普查、普查地区、人口数量、儿童人口数量、青中年人口数量、老年人口数量、大学人口数量。\n对于第x次人口���查列，请输出类似第一次人口普查、第二次人口普查。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"第x次人口普查\", \"普查地区\"], \"required\": [\"第x次人口普查\", \"普查地区\", \"人口数量\", \"儿童人口数量\", \"青中年人口数量\", \"老年人口数量\", \"大学人口数量\"], \"eval_pipeline\": {\"第x次人口普查\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"人口数量\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"儿童人口数量\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"青中年人口数量\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"老年人口数量\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"大学人口数量\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"普查地区\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
-{"instance_id":"ws_zh_054","query":"互联网巨头这几年发展怎么样？整理一下2020年-2024年这五年拼多多、腾讯、百度、阿里巴巴、京东、美团、网易这几家的总收入（以亿元为单位）、按国际财务报告准则的经营盈利（以亿元为单位）、经营利润率、每股基本盈利（以元为单位）。数据请从财报中引用。找不到的数据用“-”标记。年度不需带单位，例如2021。所有行都需要输出对应的公司，不要同一公司下的不同年份省略对应的公司名。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n公司，年度，总收入（亿元），经营盈利（亿元），经营利润率，每股基本盈利（元）。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"公司\", \"年度\"], \"required\": [\"公司\", \"年度\", \"总收入（亿元）\", \"经营盈利（亿元）\", \"经营利润率\", \"每股基本盈利（元）\"], \"eval_pipeline\": {\"每股基本盈利（元）\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"年度\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"总收入（亿元）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"经营盈利（亿元）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"经营利润率\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"公司\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
-{"instance_id":"ws_zh_055","query":"我在研究中国教育普及趋势，请你帮我整理一份2000-2024年来，中国的初中、高中(普通高中、职业高中)和普通高等教育的相关数据，包括院校数量、专任教师数量、招生数量、毕业生数量，数量单位统一为\"万\"，保留2位小数，例如10万所、7.58万人等。若某个细分教育阶段的数据官方未公布，则使用其上位总体数据代替，例如：职业高中属中等职业教育，若职业高中数据不详，则使用中等职业教育数据代替；若其上位总体数据仍不详，使用\"\/\"占位。数据需要来源于权威性的单位，例如国家统计局、国家教育部等。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：年份、教育阶段、院校数量(万所)、专任教师数量(万人)、招生数量(万人)、毕业生数量(万人)。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"年份\", \"教育阶段\"], \"required\": [\"年份\", \"教育阶段\", \"院校数量(万所)\", \"专任教师数量(万人)\", \"招生数量(万人)\", \"毕业生数量(万人)\"], \"eval_pipeline\": {\"院校数量(万所)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"专任教师数量(万人)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"招生数量(万人)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"毕业生数量(万人)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"教育阶段\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"zh"}
-{"instance_id":"ws_zh_056","query":"我对数学特别感兴趣，请你帮我整理一份教育部第四轮学科评估数学学科评级为A及以上的院校名单以及其数学系具体师资信息，请查询2025年6月官网中展示的师资信息。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：学校名称、专业名称、教师姓名、职称、研究方向、工作邮箱\n要求：若某字段在互联网上检索不到，不要脑补，在对应单元格中输出NA即可。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"教师姓名\", \"工作邮箱\"], \"required\": [\"学校名称\", \"专业名称\", \"教师姓名\", \"职称\", \"研究方向\", \"工作邮箱\"], \"eval_pipeline\": {\"教师姓名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"专业名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"职称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"研究方向\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"工作邮箱\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"学校名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"zh"}
+{"instance_id":"ws_zh_054","query":"互联网巨头这几年发展怎么样？整理一下2020年-2024年这五年拼多多、腾讯、百度、阿里巴巴、京东、美团、网易这几家的总收入（以亿元为单位）、按国际财务报告准则的经营盈利（以亿元为单位）、经营利润率、每股基本盈利（以元为单位）。数据请从财报中引用。找不到的数据用“-”标记。年度不需带单位，例如2021。所有行都需要输出对应的公司，不要同一公司下的不同年份省略对应的公司名。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n公司，年度，总收入（亿元），经营盈利（亿元），经营利润率，每股基本盈利（元）。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"公司\", \"年度\"], \"required\": [\"公司\", \"年度\", \"总收入（亿元）\", \"经营盈利（亿元）\", \"经营利润率\", \"每股基本盈利（元）\"], \"eval_pipeline\": {\"年度\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"总收入（亿元）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"经营盈利（亿元）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"经营利润率\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"每股基本盈利（元）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"公司\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
+{"instance_id":"ws_zh_055","query":"我在研究中国教育普及趋势，请你帮我整理一份2008-2024年来，中国的初中、普通高中和普通高等教育的相关数据，包括院校数量、专任教师数量、招生数量、毕业生数量，数量单位统一为\"万\"，保留2位小数，例如10万所、7.58万人等。若数据不详，使用\"\/\"占位。数据需要来源于权威性的单位，例如国家统计局、国家教育部等。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：年份、教育阶段、院校数量(万所)、专任教师数量(万人)、招生数量(万人)、毕业生数量(万人)。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"年份\", \"教育阶段\"], \"required\": [\"年份\", \"教育阶段\", \"院校数量(万所)\", \"专任教师数量(万人)\", \"招生数量(万人)\", \"毕业生数量(万人)\"], \"eval_pipeline\": {\"院校数量(万所)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"专任教师数量(万人)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"招生数量(万人)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"毕业生数量(万人)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"教育阶段\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"zh"}
+{"instance_id":"ws_zh_056","query":"我对数学特别感兴趣，请你帮我整理一份教育部第四轮学科评估数学学科评级为A+的院校名单以及其数学学院院士的具体信息。要求：1、在其数学学院任职期间已获得院士称号的才算；2、2025年之前任职的才算(不含2025年)；3、同时不包含于2025年6月之前去世的院士。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：学校名称、院士姓名、出生年份、获得奖项\n要求：若某字段在互联网上检索不到，不要脑补，在对应单元格中输出NA即可。\n获得奖项只需查找陈省身数学奖和华罗庚数学奖，没有则输出NA\n不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"院士姓名\"], \"required\": [\"学校名称\", \"院士姓名\", \"出生年份\", \"获得奖项\"], \"eval_pipeline\": {\"院士姓名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"出生年份\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.0}, \"获得奖项\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"学校名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_057","query":"我想了解2025年四川省范围内的”地方性法规”的立法情况（以公布日期为准，截止到2025年4月30日），帮我梳理相关信息，用表格给我，表头信息是法规名称、制定机关、时效性（有效\/无效）、公布日期、共几章几条。公布日期要yyyy\/zz\/nn形式。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n法规名称、制定机关、时效性、公布日期、共几章几条。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"法规名称\"], \"required\": [\"法规名称\", \"制定机关\", \"时效性\", \"公布日期\", \"共几章几条\"], \"eval_pipeline\": {\"时效性\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"共几章几条\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"制定机关\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"公布日期\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n如2025\/03\/28格式也对\\n制定机关：简称也可以\"}, \"法规名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"zh"}
-{"instance_id":"ws_zh_058","query":"筹备“中国世界遗产保护现状与价值研究”项目，需整理联合国教科文组织《世界遗产名录》中中国所有自然遗产地（包括自然遗产和自然、文化混合遗产）的基础信息（截至2024年7月第45届世界遗产委员会会议）。请收集遗产地列入年份、世界遗产正式名称、遗产地所在省份（或直辖市、自治区）、以及该地2024年春节、五一、十一分别的节假日景区收入（以亿元为单位，可以保留到小数点后一位）。如果景区有类似第一期、第二期这种分期分别申遗的情况，不同分期需要在表格中拆分不同的行展示信息。��果找不到对应数据，输出无数据即可。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n列入年份、世界遗产名称、所在省份（直辖市\/自治区）、春节景区收入(亿元)、五一景区收入(亿元)、十一景区收入(亿元)。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"列入年份\", \"世界遗产名称\"], \"required\": [\"列入年份\", \"世界遗产名称\", \"所在省份（直辖市\/自治区）\", \"春节景区收入(亿元)\", \"五一景区收入(亿元)\", \"十一景区收入(亿元)\"], \"eval_pipeline\": {\"春节景区收入(亿元)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"五一景区收入(亿元)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"十一景区收入(亿元)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"所在省份（直辖市\/自治区）\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"世界遗产名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"列入年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"zh"}
-{"instance_id":"ws_zh_059","query":"研究苹果公司产品发展历程，需要统计苹果公司从 2007 年 1 月 9 日（第一代 iPhone 发布日）至 2024 年 12 月 31 日期间，在美国市场每代主力智能手机产品线（如 iPhone, iPhone 3G, iPhone 3GS, ... iPhone 15 系列）的产品名称、首发年份、容量（如128G）、官方首发价格（合约价，美元为单位，不同的容量需要分开列价格，仅列一行，用“\/”隔开，比如容量：“4g\/8g”，售价“xxx$\/xxxm$”，要一一对应）、以及该代产品最重要的新增技术或功能特征（一项，官方宣传中普遍认可的核心亮点，如 “首款支持App Store”、“首款 Retina 显示屏”、“引入 Face ID”。统计范围仅限每年 9\/10 月发布的常规旗舰产品线（iPhone 数字系列及其衍生的 Plus\/Pro 版，不包括 SE 系列、C 系列等非主力旗舰）。每个产品线在表格中列一行，例如iPhone6和iPhone 6 Plus需要分别列一行。无法确认首发价格或核心新特性的在表格里保留空白即可。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n表格中的列名依次为：产品名称、首发年份、容量、容量对应售价（美元）、核心新增技术 \/ 功能。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"产品名称\"], \"required\": [\"产品名称\", \"首发年份\", \"容量\", \"容量对应售价（美元）\", \"核心新增技术\/功能\"], \"eval_pipeline\": {\"首发年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"容量\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"容量对应售价（美元）\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"核心新增技术\/功能\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"产品名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"zh"}
-{"instance_id":"ws_zh_060","query":"想要看下最近的大模型表现，列举在gemini2.5 pro &lite & flash-lite(区分开thinking和不开thinking模式)、claude3.7及以后发布的模型（截止到2025年6月）、o3&o3mini&o4mini、doubao-1.5-thinking和1.6thinking、deepseek v3及以后发布的主要模型（截止到2025年6月） ，具体想了解的指标包括模型号（如gemini-2.5-pro）、上下文窗口（如32k）、aime-2025指标、swe verified指标(单次尝试)、tau-bench-retail和airline指标。请尽可能在对应模型官网上搜索所有指标，但不能编造，如过官网里没有发布，相关指标可以输出NA。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n模型名称、公司、上下文窗口、AIME 2025、SWE-bench Verified、TAU-bench-Airline、Tau-bench-Retail。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"模型名称\"], \"required\": [\"模型名称\", \"公司\", \"上下文窗口\", \"aime2025\", \"swe-benchverified\", \"tau-bench-airline\", \"tau-bench-retail\"], \"eval_pipeline\": {\"aime2025\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"swe-benchverified\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"tau-bench-airline\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"tau-bench-retail\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"模型名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"公司\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"上下文窗口\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
-{"instance_id":"ws_zh_061","query":"都说\"苏妈\"挽救了AMD，请帮我整理一份苏姿丰出任AMD公司CEO推出\"Zen\"架构至2024年以来（包含2024年），AMD具体发布了哪些处理器(CPU)产品，以及产品的具体信息。如果个别信息在互联网上检索不到，不要脑补，在对应单元格中输出NA即可。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：时间，核心架构，产品系列，处理器型号，制造工艺，核心，线程，核心频率(GHz)，二级缓存(MB)，三级缓存(MB)，显卡型号，显卡核心数。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"处理器型号\"], \"required\": [\"时间\", \"核心架构\", \"产品系列\", \"处理器型号\", \"制造工艺\", \"核心\", \"线程\", \"核心频率(ghz)\", \"二级缓存(mb)\", \"三级缓存(mb)\", \"显卡型号\", \"显卡核心数\"], \"eval_pipeline\": {\"核心\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"线程\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"核心频率(ghz)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"二级缓存(mb)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"三级缓存(mb)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"显卡核心数\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"时间\": {\"preprocess\": [\"norm_date\"], \"metric\": [\"date_near\"]}, \"核心架构\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"产品系列\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"制造工艺\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"显卡型号\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"处理器型号\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
-{"instance_id":"ws_zh_062","query":"各地积极响应、组织实施\"高效办成一件事\"，北京的创新经验更是获得国务院点名表扬。请你整理一份北京市的\"高效办成一件事\"场景清单，截至2025年上半年。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：场景名称、具体事项、实施方式、牵头部门、配合部门、完成时限。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"具体事项\"], \"required\": [\"场景名称\", \"具体事项\", \"实施方式\", \"牵头部门\", \"配合部门\", \"完成时限\"], \"eval_pipeline\": {\"实施方式\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"牵头部门\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"完成时限\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"场景名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"配合部门\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"具体事项\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
-{"instance_id":"ws_zh_063","query":"最近入坑kpop了，想看看韩国四大娱乐公司（SM、JYP、YG、HYBE）在2024年12月31日之前出道的男团和女团都有哪些，以及他们在三大台（KBS、MBC、SBS）的打歌节目中第一次拿一位是什么时间？初一位时间用yyyy-mm-dd的格式表述，例如2024-01-23。初一位歌曲名称给出英文即可。小分队不算在内。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略。\n表格中的列名依次为：\n公司名称，出道年份，团体英文名，初一位时间，初一位歌曲名称。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"团体英文名\"], \"required\": [\"公司名称\", \"出道年份\", \"团体英文名\", \"初一位时间\", \"初一位歌曲名称\"], \"eval_pipeline\": {\"出道年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"团体英文名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"初一位时间\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"初一位歌曲名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"公司名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
-{"instance_id":"ws_zh_064","query":"好久没有看书了，不知道现在市面上有什么值得读的好书，请你帮我搜集整理一份21世纪以来到2024年（包含2024年）的茅盾文学奖获奖作品、鲁迅文学奖、宝铂文学奖的获奖作品，以及相关信息。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：所获奖项、作品名称、作品体裁、作者名称、发表年份、刊登期刊\/出版社。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"作品名称\"], \"required\": [\"所获奖项\", \"作品名称\", \"作品体裁\", \"作者名称\", \"发表年份\", \"刊登期刊\/出版社\"], \"eval_pipeline\": {\"作品体裁\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"发表年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"作者名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"刊登期刊\/出版社\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"所获奖项\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"作品名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"zh"}
+{"instance_id":"ws_zh_058","query":"筹备“中国世界遗产保护现状与价值研究”项目，需整理联合国教科文组织《世界遗产名录》中中国所有自然遗产地（包括自然遗产和自然、文化混合遗产）的基础信息（截至2024年7月第46届世界遗产委员会会议，包含第46届）。请收集遗产地列入年份、世界遗产正式名称、遗产地所在省份（或直辖市、自治区）、遗产类别。如果景区有类似第一期、第二期这种分期分别申遗的情况，不同分期需要在表格中拆分不同的行展示信息。如果找不到对应数据，输出无数据即可。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n列入年份、世界遗产名称、所在省份（直辖市\/自治区）、遗产类别。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"列入年份\", \"世界遗产名称\"], \"required\": [\"列入年份\", \"世界遗产名称\", \"所在省份（直辖市\/自治区）\", \"遗产类别\"], \"eval_pipeline\": {\"所在省份（直辖市\/自治区）\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"世界遗产名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"列入年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"遗产类别\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
+{"instance_id":"ws_zh_059","query":"研究苹果公司产品发展历程，需要统计苹果公司从 2007 年 1 月 9 日（第一代 iPhone 发布日）至 2024 年 12 月 31 日期间，在美国市场每代主力智能手机产品线（如 iPhone, iPhone 3G, iPhone 3GS, ... iPhone 15 系列）的产品名称、首发年份、容量（如128G）、官方首发价格（合约价，美元为单位，不同的容量需要分开列价格，仅列一行，用“\/”隔开，比如容量：“4g\/8g”，售价“xxx$\/xxxm$”，要一一对应）、以及该代产品最重要的新增技术或功能特征（一项，官方宣传中普遍认可的核心亮点，如 “首款支持App Store”、“首款 Retina 显示屏”、“引入 Face ID”。统计范围仅限每年 9\/10 月发布的常规旗舰产品线（iPhone 数字系列及其衍生的 Plus\/Pro 版，不包括 SE 系列、C 系列、mini系列等非主力旗舰）。每个产品线在表格中列一行，例如iPhone6和iPhone 6 Plus需要分别列一行。无法确认首发价格或核心新特性的在表格里保留空白即可。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n表格中的列名依次为：产品名称、首发年份、容量、容量对应售价（美元）、核心新增技术 \/ 功能。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"产品名称\"], \"required\": [\"产品名称\", \"首发年份\", \"容量\", \"容量对应售价（美元）\", \"核心新增技术\/功能\"], \"eval_pipeline\": {\"首发年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"容量\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"容量对应售价（美元）\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"核心新增技术\/功能\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n只答出其中一个可算对\"}, \"产品名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"zh"}
+{"instance_id":"ws_zh_060","query":"想要看下最近的大模型表现，列举在gemini2.5 pro &lite & flash-lite(区分开thinking和不开thinking模式)、claude3.7及以后发布的模型（截止到2025年6月）、o3&o3mini&o4mini、doubao-1.5-thinking和1.6thinking、deepseek v3及以后发布的主要模型（以上所有模型截止到2025年6月） ，具体想了解的指标包括模型号（如gemini-2.5-pro）、上下文窗口（如32k）、aime-2025指标、swe verified指标(单次尝试)、tau-bench-retail和airline指标。请尽可能在对应模型官网上搜索所有指标，但不能编造，如过官网里没有发布，相关指标可以输出NA。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n模型名称、公司、上下文窗口、AIME 2025、SWE-bench Verified���TAU-bench-Airline、Tau-bench-Retail。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"模型名称\"], \"required\": [\"模型名称\", \"公司\", \"上下文窗口\", \"aime2025\", \"swe-benchverified\", \"tau-bench-airline\", \"tau-bench-retail\"], \"eval_pipeline\": {\"aime2025\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"swe-benchverified\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"tau-bench-airline\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"tau-bench-retail\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"模型名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"公司\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"上下文窗口\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
+{"instance_id":"ws_zh_061","query":"都说\"苏妈\"挽救了AMD，请帮我整理一份苏姿丰出任AMD公司CEO推出\"Zen\"架构至2023年以来（包含2023年），AMD具体发布了哪些处理器(CPU)产品，以及产品的具体信息。如果个别信息在互联网上检索不到，不要脑补，在对应单元格中输出NA即可。核心频率采用基准时钟频率。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：时间，核心架构，产品系列，处理器型号，制造工艺，核心，线程，核心频率(GHz)，二级缓存(MB)，三级缓存(MB)，显卡型号，显卡核心数。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"处理器型号\"], \"required\": [\"时间\", \"核心架构\", \"产品系列\", \"处理器型号\", \"制造工艺\", \"核心\", \"线程\", \"核心频率(ghz)\", \"二级缓存(mb)\", \"三级缓存(mb)\", \"显卡型号\", \"显卡核心数\"], \"eval_pipeline\": {\"核心\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"线程\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"二级缓存(mb)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"三级缓存(mb)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.01}, \"显卡核心数\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.01}, \"时间\": {\"preprocess\": [\"norm_date\"], \"metric\": [\"date_near\"]}, \"核心架构\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"产品系列\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"制造工艺\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"显卡型号\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"处理器型号\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"核心频率(ghz)\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
+{"instance_id":"ws_zh_062","query":"各地积极响应、组织实施\"高效办成一件事\"，北京的创新经验更是获得国务院点名表扬。请你整理一份北京市的\"高效办成一件事\"场景清单，截至2025年上半年。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。请注意，关于2025年发布的第四批“高效办成一件事”，每个场景下，不同的具体事项请分行拆开输出。\n表格中的列名依次为：场景名称、具体事项、实施方式、牵头部门、配合部门、完成时限。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"具体事项\"], \"required\": [\"场景名称\", \"具体事项\", \"实施方式\", \"牵头部门\", \"配合部门\", \"完成时限\"], \"eval_pipeline\": {\"实施方式\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"牵头部门\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"完成时限\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"场景名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"配合部门\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"具体事项\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
+{"instance_id":"ws_zh_063","query":"最近入坑kpop了，想看看韩国娱乐公司（SM、JYP）在2000年（不含2000年）之后到2025年之前（不含2025年）出道的男团和女团都有哪些，以及他们在三大台（KBS、MBC、SBS）的打歌节目中第一次拿一位是什么时间？初一位时间只给出年份即可，例如2024。初一位歌曲名称给出英文即可。小分队、子团不算在内。我不喜欢NCT请剔除出去。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略。\n表格中的列名依次为：\n公司名称，出道年份，团体英文名，初一位时间，初一位歌曲名称。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"团体英文名\"], \"required\": [\"公司名称\", \"出道年份\", \"团体英文名\", \"初一位时间\", \"初一位歌曲名称\"], \"eval_pipeline\": {\"出道年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"团体英文名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"初一位时间\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"初一位歌曲名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"公司名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
+{"instance_id":"ws_zh_064","query":"好久没有看书了，不知道现在市面上有什么值得读的好书，请你帮我搜集整理一份21世纪以来到2024年（包含2024年）的茅盾文学奖获奖作品、鲁迅文学奖、宝铂文学奖的获奖作品，以及相关信息。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：所获奖项、作品名称、作品体裁、作者名称、发表年份、刊登期刊\/出版社。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"作品名称\"], \"required\": [\"所获奖项\", \"作品名称\", \"作品体裁\", \"作者名称\", \"发表年份\", \"刊登期刊\/出版社\"], \"eval_pipeline\": {\"发表年份\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.0}, \"作者名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"刊登期刊\/出版社\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"所获奖项\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"作品体裁\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"作品名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_065","query":"最近真的想买辆车了，查下2025年6月，国内比亚迪王��系列中名称带有智驾版的所有车型的参数信息，要不同车型中最便宜的那款配置就行。参数信息需要包含：车型名称、排量[L]、供油方式、发动机最大功率[kW]、发动机最大扭矩[N·m]、发动机最大马力[Ps]、发动机进气方式、发动机气缸数、电池类型、电芯品牌、电动机总功率[kW]、电动机总马力[Ps]、电动机总扭矩[N·m]、电池容量。没有查询到的信息返回\"-\"。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：车型名称，排量[L]，供油方式，发动机最大功率[kW]，发动机最大扭矩[N·m]，发动机最大马力[Ps]，发动机进气方式，发动机气缸数，电池类型，电芯品牌，电动机总功率[kW]，电动机总马力[Ps]，电动机总扭矩[N·m]，电池容量。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"车型名称\"], \"required\": [\"车型名称\", \"排量[l]\", \"供油方式\", \"发动机最大功率[kw]\", \"发动机最大扭矩[n·m]\", \"发动机最大马力[ps]\", \"发动机进气方式\", \"发动机气缸数\", \"电池类型\", \"电芯品牌\", \"电动机总功率[kw]\", \"电动机总马力[ps]\", \"电动机总扭矩[n·m]\", \"电池容量\"], \"eval_pipeline\": {\"排量[l]\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"发动机最大功率[kw]\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"发动机最大扭矩[n·m]\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"发动机最大马力[ps]\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"发动机进气方式\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"发动机气缸数\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"电芯品牌\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"电动机总功率[kw]\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"电动机总马力[ps]\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"电动机总扭矩[n·m]\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"电池容量\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.01}, \"车型名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"供油方式\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"电池类型\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_066","query":"想看下电商最近几年大促的表现，帮我统计下2020年到2024年（包含2020年和2024年）拼多多、淘宝天猫、京东三大平台的618和双11大促的gmv(人民币)、成交破亿的品牌数量。用官方的数据，如果没有官方发布的数据，单元格中注明“官方未公布”即可。618和双11大促的gmv单位为亿，需输出数字和单位，如“xxx亿”；成交破亿的品牌数量单位为个，但不必带单位，直接输出数字即可。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n电商平台、年份、618 GMV、618成交破亿的品牌数量、双11 GMV、双11成交破亿的品牌数量。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"电商平台\", \"年份\"], \"required\": [\"电商平台\", \"年份\", \"618gmv\", \"618成交破亿的品牌数量\", \"双11gmv\", \"双11成交破亿的品牌数量\"], \"eval_pipeline\": {\"电商平台\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"618gmv\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.0001}, \"618成交破亿的品牌数量\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.0001}, \"双11gmv\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.0001}, \"双11成交破亿的品牌数量\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.0001}}}","language":"zh"}
 {"instance_id":"ws_zh_067","query":"北京二手房房价真的跌麻了吗，都说2022年是顶峰了，���月维度整理一下2022年1月到2025年5月北京昌平区 朝阳区 大兴区 东城区 房山区 丰台区 海淀区 怀柔区 门头沟区 密云区 平谷区 石景山区 顺义区 通州区 西城区 延庆区的平均月度房价，数据来源用安居客的数据。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n区域、2022\/01房价、2022\/02房价... 2025\/05房价。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"区域\"], \"required\": [\"区域\", \"2022\/01房价\", \"2022\/02房价\", \"2022\/03房价\", \"2022\/04房价\", \"2022\/05房价\", \"2022\/06房价\", \"2022\/07房价\", \"2022\/08房价\", \"2022\/09房价\", \"2022\/10房价\", \"2022\/11房价\", \"2022\/12房价\", \"2023\/01房价\", \"2023\/02房价\", \"2023\/03房价\", \"2023\/04房价\", \"2023\/05房价\", \"2023\/06房价\", \"2023\/07房价\", \"2023\/08房价\", \"2023\/09房价\", \"2023\/10房价\", \"2023\/11房价\", \"2023\/12房价\", \"2024\/01房价\", \"2024\/02房价\", \"2024\/03房价\", \"2024\/04房价\", \"2024\/05房价\", \"2024\/06房价\", \"2024\/07房价\", \"2024\/08房价\", \"2024\/09房价\", \"2024\/10房价\", \"2024\/11房价\", \"2024\/12房价\", \"2025\/01房价\", \"2025\/02房价\", \"2025\/03房价\", \"2025\/04房价\", \"2025\/05房价\"], \"eval_pipeline\": {\"2022\/01房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2022\/02房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2022\/03房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2022\/04房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2022\/05房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2022\/06房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2022\/07房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2022\/08房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2022\/09房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2022\/10房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2022\/11房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2022\/12房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2023\/01房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2023\/02房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2023\/03房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2023\/04房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2023\/05房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2023\/06房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2023\/07房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2023\/08房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2023\/09房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2023\/10房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2023\/11房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2023\/12房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2024\/01房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2024\/02房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2024\/03房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2024\/04房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2024\/05房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2024\/06房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2024\/07房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2024\/08房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2024\/09房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2024\/10房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2024\/11房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2024\/12房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2025\/01房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2025\/02房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2025\/03房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2025\/04房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"2025\/05房价\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"区域\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"zh"}
-{"instance_id":"ws_zh_068","query":"盘点一下中国央行从2020年到2025年上半年，历次调整基准利率的操作，我需要的信息包括调整年份、调整生效日期、调整前的一年期LPR、调整后的一年期LPR、调整前的五年期LPR、调整后的五年期LPR、调整前的7天逆回购操作利率、调整后的7天逆回购操作利率、调整前的金融机构存款准备金率、调整后的金融机存款准备金率。LPR、7天逆回购操作利率和金融机构加权平均存款准备金率若不是同一天调整，需单独提行，未调整的数据标记为“NA”。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n调整年份、调整生效日期、调整前的一年期LPR、调整后的一年期LPR、调整前的五年期LPR、调整后的五年期LPR、调整前的7天逆回购操作利率、调整后的7天逆回购操作利率、调整前的金融机构存款准备金率、调整后的金融机构存款准备金率。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"调整年份\", \"调整生效日期\"], \"required\": [\"调整年份\", \"调整生效日期\", \"调整前的一年期lpr\", \"调整后的一年期lpr\", \"调整前的五年期lpr\", \"调整后的五年期lpr\", \"调整前的7天逆回购操作利率\", \"调整后的7天逆回购操作利率\", \"调整前的金融机构存款准备金率\", \"调整后的金融机构存款准备金率\"], \"eval_pipeline\": {\"调整前的一年期lpr\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"调整后的一年期lpr\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"调整前的五年期lpr\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"调整后的五年期lpr\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"调整前的7天逆回购操作利率\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"调整后的7天逆回购操作利率\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"调整前的金融机构存款准备金率\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"调整后的金融机构存款准备金率\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"调整年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"调整生效日期\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
-{"instance_id":"ws_zh_069","query":"最近想要赶上大模型潮流，需要恶补一下大模型知识，帮我整理字节的seed团队和deepseek这2023年-2025年上半年（截至6月30日）发表的大模型相关论文，请在seed团队以及deepseek公司官网查询。包括发表日期（yyyy-mm-dd格式，例如2024-02-01），论文名字和主要作者；若有呈现内容完全一致的两篇论文，最终时间以arXiv平台发布时间为准；论文名字和主要作者保持论文上发表的英文即可，谢谢。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略。\n表格中的列名依次为：\n公司名称、发表日期、论文名称、论文作者。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"论文名称\"], \"required\": [\"公司名称\", \"发表日期\", \"论���名称\", \"论文作者\"], \"eval_pipeline\": {\"发表日期\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"公司名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"论文作者\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"论文名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
-{"instance_id":"ws_zh_070","query":"统计下从2001年春晚有收视率以来到2025年（包含2001年和2025年），每年的春晚总台电视端收视率是多少，总导演是谁，以及最后倒计时时冠名商是谁。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n年份、电视端收视率、导演、冠名商。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"年份\"], \"required\": [\"年份\", \"电视端收视率\", \"导演\", \"冠名商\"], \"eval_pipeline\": {\"年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"电视端收视率\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"导演\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"冠名商\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
-{"instance_id":"ws_zh_071","query":"在做韩国上映的电影历年票房前三的电影盘点，整理一下这个国家2010年到2024年这15年间电影院上映的电影，历年票房（此处指的是整体累积票房）前三的电影、导演、领衔主演、票房及观影人次和题材信息。累积票房以十亿韩元为单位，保留整数。找不到的用“NA”标注。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n上映年份、电影名、题材、导演、领衔主演、累积票房、累积观影人次。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"电影名\"], \"required\": [\"上映年份\", \"电影名\", \"题材\", \"导演\", \"领衔主演\", \"累积票房（十亿韩元）\", \"累积观影人次\"], \"eval_pipeline\": {\"累积票房（十亿韩元）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"累积观影人次\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"电影名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"题材\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n答出参考答案中的部分类型（即子集）即视为正确、基于权威来源及官方依据的类型标注同样正确、答出其中一个子集其他类型内容合理也视为正确\"}, \"领衔主演\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n接受答出的是参考答案的子集。\"}, \"上映年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"导演\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n全称和简称输出都可以\"}}}","language":"zh"}
+{"instance_id":"ws_zh_068","query":"盘点一下中国央行从2020年到2025年上半年，历次调整基准利率的操作，我需要的信息包括调整年份、调整生效日期、调整前的一年期LPR、调整后的一年期LPR、调整前的五年期LPR、调整后的五年期LPR、调整前的7天逆回购操作利率、调整后的7天逆回购操作利率、调整前的金融机构存款准备金率、调整后的金融机存款准备金率。LPR、7天���回购操作利率和金融机构加权平均存款准备金率若不是同一天调整，需单独提行，未调整的数据标记为“NA”。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n利率和lpr等单元格内，保留百分号，例如xx%。\n表格中的列名依次为：\n调整年份、调整生效日期、调整前的一年期LPR、调整后的一年期LPR、调整前的五年期LPR、调整后的五年期LPR、调整前的7天逆回购操作利率、调整后的7天逆回购操作利率、调整前的金融机构存款准备金率、调整后的金融机构存款准备金率。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"调整年份\", \"调整生效日期\"], \"required\": [\"调整年份\", \"调整生效日期\", \"调整前的一年期lpr\", \"调整后的一年期lpr\", \"调整前的五年期lpr\", \"调整后的五年期lpr\", \"调整前的7天逆回购操作利率\", \"调整后的7天逆回购操作利率\", \"调整前的金融机构存款准备金率\", \"调整后的金融机构存款准备金率\"], \"eval_pipeline\": {\"调整前的一年期lpr\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"调整后的一年期lpr\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"调整前的五年期lpr\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"调整后的五年期lpr\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"调整前的7天逆回购操作利率\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"调整后的7天逆回购操作利率\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"调整前的金融机构存款准备金率\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"调整后的金融机构存款准备金率\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"调整年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"调整生效日期\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
+{"instance_id":"ws_zh_069","query":"最近想要赶上大模型潮流，需要恶补一下大模型知识，帮我整理字节的seed团队和deepseek在2023年1月1日-2025年6月30日之间发表的大模型相关论文，请在seed团队以及deepseek公司官网查询（注意，只要是seed团队参与的论文都可以被计算在内）。包括发表日期（yyyy-mm-dd格式，例如2024-02-01），论文名字和主要作者；若有呈现内容完全一致的两篇论文，最终时间以arXiv平台发布时间（初次submit时间）为准；论文名字和主要作者保持论文上发表的英文即可，谢谢。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略。\n表格中的列名依次为：\n公司名称、发表日期、论文名称、论文作者。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"论文名称\"], \"required\": [\"公司名称\", \"发表日期\", \"论文名称\", \"论文作者\"], \"eval_pipeline\": {\"发表日期\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"公司名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"论文作者\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"论文名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
+{"instance_id":"ws_zh_070","query":"统计下从2001年春晚有收视率以来到2025年（包含2001年和2025年），每年的春晚总台电视端收视率是多少，总导演是谁，以及零点倒计时冠名商是谁。查询不到的标注为\"-\"。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n年份、电视端收视率、导演、冠名商。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"年份\"], \"required\": [\"年份\", \"电视端���视率\", \"导演\", \"冠名商\"], \"eval_pipeline\": {\"年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"电视端收视率\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"导演\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"冠名商\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
+{"instance_id":"ws_zh_071","query":"在做韩国上映的电影历年票房前三的电影盘点，整理一下这个国家2010年到2024年这15年间电影院上映的电影，历年票房（此处指的是整体累积票房）前三的电影、导演、领衔主演、票房及观影人次和题材信息。累积票房以十亿韩元为单位，保留整数。观影人次具体到个位数。找不到的用“NA”标注。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n上映年份、电影名、题材、导演、领衔主演、累积票房、累积观影人次。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"电影名\"], \"required\": [\"上映年份\", \"电影名\", \"题材\", \"导演\", \"领衔主演\", \"累积票房（十亿韩元）\", \"累积观影人次\"], \"eval_pipeline\": {\"累积票房（十亿韩元）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"累积观影人次\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.1}, \"电影名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"题材\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n答出参考答案中的部分类型（即子集）即视为正确、基于权威来源及官方依据的类型标注同样正确、答出其中一个子集其他类型内容合理也视为正确\"}, \"领衔主演\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n接受答出的是参考答案的子集。\\n电影名称：电影名称不需要完全一致，有别的翻译也可接受，或者只给出了电影名+第几部如：将复仇者联盟3：无限战争说出复仇者联盟3也可接受\"}, \"上映年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"导演\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n全称和简称输出都可以\"}}}","language":"zh"}
 {"instance_id":"ws_zh_072","query":"我是重度恋综爱好者，给我整理一下爱奇艺、腾讯、芒果、优酷这几个平台制作并独播的最近五年（2020-2024年）所有恋综，不是婚姻类的，要谈恋爱的。还要节目中牵手成功的男女嘉宾名字（有全名写全名，不要昵称），节目外在一起的不算，男女名字用&连接，如阿珍&阿强，如果有多对cp牵手成功，用顿号隔开，以及我还想知道，截止到2024年年底，有多少cp还在一起，只要是没官宣分手的都默认还在一起。若该节目没有牵手成功cp或所有cp都分手则填\"无\"。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n平台名、恋综名称、节目牵手嘉宾、仍未分手cp。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"恋综名称\"], \"required\": [\"平台名\", \"恋综名称\", \"节目牵手嘉宾\", \"仍未分手cp\"], \"eval_pipeline\": {\"恋综名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"节目牵手嘉宾\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"仍未分手cp\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"平台名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n芒果和芒果tv都对\"}}}","language":"zh"}
 {"instance_id":"ws_zh_073","query":"北京市文物局官网上收录的北京所有免费博物馆列表整理一下，以及具体地址和开放时间。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n博物馆名称、地址、开放时间。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"博物馆名称\"], \"required\": [\"博物馆名称\", \"地址\", \"开放时间\"], \"eval_pipeline\": {\"地址\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"开放时间\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"博物馆名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"zh"}
-{"instance_id":"ws_zh_074","query":"浪姐1-6季每季公演（一公-五公）的节目（不含个人表演，特指多个姐姐组成的团体表演）、表演的姐姐、及其票数都是什么？如果没有具体票数，可以用“-”代替。不同表演节目需要分行输出，其中每一行都需要输出季数、公演次数等信息，不得合并单元格。注意也不包含赛前秀、开场秀、赛点秀、合作秀。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n季数、公演次数、节目名称、参演姐姐、票数。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"节目名称\"], \"required\": [\"季数\", \"公演次数\", \"节目名称\", \"参演姐姐\", \"票数\"], \"eval_pipeline\": {\"季数\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"公演次数\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"票数\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"参演姐姐\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"节目名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
+{"instance_id":"ws_zh_074","query":"浪姐1-6季每季公演（一公-五公）的节目（不含个人表演，特指多个姐姐组成的团体表演）、表演的姐姐、及其票数都是什么？票数不包含加成例如合作秀、赛前秀等加成，如果没有具体票数，可以用“-”代替。不同表演节目需要分行输出，其中每一行都需要输出季数、公演次数等信息，不得合并单元格。注意节目不包含赛前秀、开场秀、赛点秀、合作秀。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n季数、公演次数、节目名称、参演姐姐、票数。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"节目名称\"], \"required\": [\"季数\", \"公演次数\", \"节目名称\", \"参演姐姐\", \"票数\"], \"eval_pipeline\": {\"公演次数\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"票数\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"季数\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.0}, \"参演姐姐\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"节目名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_075","query":"帮我统计下2024年1月-2024年12月在Netflix播出的韩剧，需要了解作品名称，开播时间，作品导演，作品集数，作品及作品演员、导演等因该作品获得的奖项（只要百想艺术和青龙的奖项，提名的也算）。作品集数列单位为集，填写需输出数字和单位，如“xx集”；若没有获得任何奖项，则用“\/”代替。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n作品名称，开播时间，作品导演，作品集数，作品相关获奖记录。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"作品名称\", \"开播时间\"], \"required\": [\"作品名称\", \"开播时间\", \"作品导演\", \"作品集数\", \"作品相关获奖记录\"], \"eval_pipeline\": {\"作品名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"作品导演\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n涉及有别名的时候，接受模型回答是参考答案的子集\"}, \"作品集数\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"作品相关获奖记录\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"开播时间\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_076","query":"我看到一则新闻是国内重点上市车企2024年的市值排名前三的是比亚迪、上汽集团、长城汽车，我粗略地查了一下这三家车企都是在2011年上市的。所以请你帮我整理一下这三家车企2011-2014年（包含2011年和2014年）的年报数据。具体包括以下维度：\n企业名称\n营业收入（元）\n归属于上市公司股东的净利润（元）\n经营活动产生的现金流量净额（元）\n基本每股收益（元\/股）\n加权平均净资产收益率（%）\n\n请你帮我整理成直观的表格，如有数据无法查到，请标记“-”。请注意，每一年的数据分行输出，其中每一行都需要完整包含企业名称等数据，不得随意合并单元格。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n企业名称、财年、营业收入（元）、归属于上市公司股东的净利润（元）、基本每股收益（元\/股）、加权平均净资产收益率（%）。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"企业名称\", \"财年\"], \"required\": [\"企业名称\", \"财年\", \"营业收入（元）\", \"归属于上市公司股东的净利润（元）\", \"基本每股收益（元\/股）\", \"加权平均净资产收益率（%）\"], \"eval_pipeline\": {\"营业收入（元）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"归属于上市公司股东的净利润（元）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"基本每股收益（元\/股）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"加权平均净资产收益率（%）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"企业名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"财年\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
-{"instance_id":"ws_zh_077","query":"我想了解一下人工智能立法的发展情况，帮我在知网检索“数字法学女王”中国政法大学张凌寒教授在2023年1月1日到2025年5月31日期间发表的所有论文，用表格形式给我，表头名称需包括：论文题目、作者、发表时间、发表刊物、关键词、DOI号、基金资助。如果没有基金资助，请输出无基金。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：论文题目、作者、发表时间、发表刊物、关键词、DOI号、基金资助。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"论文题目\"], \"required\": [\"论文题目\", \"作者\", \"发表时间\", \"发表刊物\", \"关键词\", \"doi号\", \"基金资助\"], \"eval_pipeline\": {\"发表时间\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"doi号\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"作者\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"发表刊物\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"基金资助\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"关键词\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n关键词需全部答出，不需要完全一致，意思大致相同即可\"}, \"论文题目\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"zh"}
+{"instance_id":"ws_zh_077","query":"我想了解一下人工智能立法的发展情况，帮我在知网检索“数字法学女王”中国政法大学张凌寒教授在2023年1月1日到2025年5月31日期间发表的所有论文，用表格形式给我，表头名称需包括：论文题目、作者、发表时间、发表刊物、关键词、DOI号、基金资助。如果没有基金资助，请输出无基金，如果没有doi号，请输出nan。发表时间请遵循yyyy\/mm\/dd的格式。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：论文题目、作者、发表时间、发表刊物、关键词、DOI号、基金资助。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"论文题目\"], \"required\": [\"论文题目\", \"作者\", \"发表时间\", \"发表刊物\", \"关键词\", \"doi号\", \"基金资助\"], \"eval_pipeline\": {\"发表时间\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"doi号\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"作者\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"发表刊物\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"基金资助\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"关键词\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n关键词需全部答出，不需要完全一致，意思大致相同即可\"}, \"论文题目\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"zh"}
 {"instance_id":"ws_zh_078","query":"随着中国互联网的发展，网络谣言也是层出不穷。我看到中国互联网联合辟谣平台有个很有意思的板块，叫“今日辟谣”。\n那请你帮我整理一下2025年5月的今日辟谣板块的内容，按照时间，辟谣\/谣言内容，详情\/真相给我整理出来。时间格式采用xxxx年x月x日,例如2025年5月19日。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n时间，辟谣\/谣言内容，详情\/真相。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"辟谣\/谣言内容\"], \"required\": [\"时间\", \"辟谣\/谣言内容\", \"详情\/真相\"], \"eval_pipeline\": {\"时间\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"辟谣\/谣言内容\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"详情\/真相\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字���应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_079","query":"中国31个省市行政区划（不包括港澳两个特别行政区和台湾省）的一把手一般轮转时长是多久？请统计一下从2015年到2024年期间（包含2015年和2024年），各省省委书记\/直辖市市委书记的姓名、上任日期、离任日期。日期以yyyy-mm的形式给出，例如2022-02。请注意，凡是2015年-2024年期间担任过一把手的，均需要被统计进去。如果截止到2024年年底，该名官员仍然在任，则离任日期给出“2024年底在任”即可。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n行政区划、姓名、上任日期、离任日期。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"行政区划\", \"姓名\"], \"required\": [\"行政区划\", \"姓名\", \"上任日期\", \"离任日期\"], \"eval_pipeline\": {\"行政区划\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"姓名\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"上任日期\": {\"preprocess\": [\"norm_date\"], \"metric\": [\"date_near\"]}, \"离任日期\": {\"preprocess\": [\"norm_date\"], \"metric\": [\"date_near\"]}}}","language":"zh"}
 {"instance_id":"ws_zh_080","query":"想了解下中国餐饮企业近几年的布局，帮我看看霸王茶姬、蜜雪冰城、奈雪的茶、古茗、茶百道和沪上阿姨这几家，截至2024年12月31日，各自的全球门店数量、海外门店数量、北上广深门店数量、新一线门店数量、二线门店数量、三线及以下门店数量。请从招股书或财报等官方数据中获取，如果部分信息获取不到，标记NA。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n企业名称、全球门店数量、海外门店数量、北上广深门店数量、新一线门店数量、二线门店数量、三线及以下门店数量。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"企业名称\"], \"required\": [\"企业名称\", \"全球门店数量\", \"海外门店数量\", \"北上广深门店数量\", \"新一线门店数量\", \"二线门店数量\", \"三线及以下门店数量\"], \"eval_pipeline\": {\"全球门店数量\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.0}, \"海外门店数量\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.0}, \"北上广深门店数量\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.0}, \"新一线门店数量\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.0}, \"二线门店数量\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.0}, \"三线及以下门店数量\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.0}, \"企业名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"zh"}
 {"instance_id":"ws_zh_081","query":"我读了迟子建的《额尔古纳河右岸》，对鄂温克族文化产生了浓厚兴趣，计划写一篇公众号文章。为充实内容，需要搜集相关学术资料。\n请帮我在万方数据库检索 2005–2024年 发表的期刊论文，题目里必须包含“鄂温克族”。\n整理每篇论文的以下信息：发表年份、期刊名称、论文标题、关键词、摘要，日期按照从远到近进行排列。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n发表年份\n期刊名称\n论文标题\n关键词\n摘要。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"期刊名称\", \"论文标题\"], \"required\": [\"发表年份\", \"期刊名称\", \"论文标题\", \"关键词\", \"摘要\"], \"eval_pipeline\": {\"发表年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"摘要\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"关键词\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"期刊名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"论文标题\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
-{"instance_id":"ws_zh_082","query":"最近想买相机拍拍风景，听说尼康拍风景还不错，想买个微单，帮我统计一下尼康截至2025年上半年所有Z系列产品的信息吧，我想了解的信息有：有效像素、重量(单机身+电池+存储卡的重量)、最高每秒拍摄幅数、快门速度范围、最高对焦点数目、数码影像处理器、ISO范围(不依靠曝光补偿的原生ISO范围)、相机内减震技术(电子VR减震不用输出)\n有效像素的输出格式为：约XXXX万\n重量的输出格式为：约XXXg\n最高每秒拍摄幅数的输出格式为：约XXX幅\/秒\n相机名称的输出格式为：Z+相机系列，如：Z5、Z6、Z6Ⅱ\n最高对焦点数目的输出格式为：XXX个。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：相机名称、有效像素、重量、最高每秒拍摄幅数、快门速度范围、最高对焦点数目、数码影像处理器、ISO范围、相机内减震技术。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"相机名称\"], \"required\": [\"相机名称\", \"有效像素\", \"重量\", \"最高每秒拍摄幅数\", \"快门速度范围\", \"最高对焦点数目\", \"数码影像处理器\", \"iso范围\", \"相机内减震技术\"], \"eval_pipeline\": {\"相机名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"最高对焦点数目\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"快门速度范围\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n括号里的内容未提及也算对\"}, \"数码影像处理器\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"iso范围\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"相机内减震技术\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"有效像素\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"重量\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"最高每秒拍摄幅数\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
+{"instance_id":"ws_zh_082","query":"最近想买相机拍拍风景，听说尼康拍风景还不错，想买个微单，帮我统计一下尼康截至2025年上半年所有Z系列产品的信息吧，我想了解的信息有：有效像素、重量(单机身+电池+存储卡的重量)、最高每秒拍摄幅数、快门速度范围、最高对焦点数目、数码影像处理器、ISO范围(不依靠曝光补偿的原生ISO范围)、相机内减震技术(电子VR减震不用输出)\n有效像素的输出格式为：约XXXX万\n重量的输出格式为：约XXXg\n最高每秒拍摄幅数的输出格式为：约XXX幅\/秒\n相机名称的输出格式为：Z+相机系列，如：Z5、Z6、Z6Ⅱ\n最高对焦点数目的输出格式为：XXX个。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：相机名称、有效像素、重量、最高每秒拍摄幅数、快门速度范围、最高对焦点数目、数码影像处理器、ISO范围、相机内减震技术。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"相机名称\"], \"required\": [\"相机名称\", \"有效像素\", \"重量\", \"最高每秒拍摄幅数\", \"快门速度范围\", \"最高对焦点数目\", \"数码影像处理器\", \"iso范围\", \"相机内减震技术\"], \"eval_pipeline\": {\"相机名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"最高对焦点数目\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"快门速度范围\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n括号里的内容未提及也算对\\n快门速度范围：快门速度最高或者最低范围可以根据实际给出的进行调整，如：1\/8000至30秒（以1\/3或1\/2ev为步长进行微调，m模式下可扩展至900s），可以写成1\/8000至30秒也可以写成1\/8000至900秒\"}, \"数码影像处理器\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"iso范围\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"相机内减震技术\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"有效像素\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"重量\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"最高每秒拍摄幅数\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_083","query":"毕业了想去中国的\"西南F4\"旅游一圈，截至2024年末，帮我看看他们有哪些5A级景区？以表格的形式帮我梳理一份5A景区清单，包括景区名称、所在城市、旺季门票成人价格、旺季开放时间。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：景区名称、所在城市、旺季景区票价(元\/人)、旺季开放时间。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"景区名称\"], \"required\": [\"景区名称\", \"所在城市\", \"旺季景区票价(元\/人)\", \"旺季开放时间\"], \"eval_pipeline\": {\"旺季景区票价(元\/人)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"所在城市\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"旺季开放时间\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"景区名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"zh"}
 {"instance_id":"ws_zh_084","query":"在研究中国的游戏市场发展，请你帮我搜集整理一下2015-2024年来（包含2015年和2024年）中国游戏产业相关数据，包括年份、平台类型(移动游戏、客户端游戏、网页游戏、主机游戏)、用户规模（亿）、市场收入（亿元）。其中，\"用户规模\"与\"市场收入\"按照平台类型分别汇总，不需要汇总整体的数据，分别给移动游戏、客户端游戏、网页游戏、主机游戏四种平台的数据即可。查询不到的返回-。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：年份，平台类型，用户规模（亿），市场收入（亿元）。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"年份\", \"平台类型\"], \"required\": [\"年份\", \"平台类型\", \"用户规模（亿）\", \"市场收入（亿元）\"], \"eval_pipeline\": {\"用户规模（亿）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"市场收入（亿元）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"平台类型\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
-{"instance_id":"ws_zh_085","query":"2025年1月1日-5月31日竣工落地的一带一路中企海外项目都有哪些？请全量检索整理一下相关信息，包括名称、承包公司、工程启动时间（正式开始建设生产的时间）和竣工投运时间，精确到月即可，例如2025年2月。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n国家、项目名称、承包公司、启动时间、竣工时间。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"项目名称\", \"竣工时间\"], \"required\": [\"国家\", \"项目名称\", \"承包公司\", \"启动时间\", \"竣工时间\"], \"eval_pipeline\": {\"竣工时间\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"国家\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"启动时间\": {\"preprocess\": [\"norm_date\"], \"metric\": [\"date_near\"]}, \"项目名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n31号、33号、41号公路升级改造项目和柬埔寨三条公路升级改造项目是同一个项目\"}, \"承包公司\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
+{"instance_id":"ws_zh_085","query":"2025年1月1日-5月31日竣工落地的一带一路中企海外项目都有哪些？请全量检索整理一下相关信息，包括名称、承包公司、工程启动时间（正式开始建设生产的时间）和竣工投运时间，精确到月即可，例如2025年2月。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n国家、项目名称、承包公司、启动时间、竣工时间。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"项目名称\", \"竣工时间\"], \"required\": [\"国家\", \"项目名称\", \"承包公司\", \"启动时间\", \"竣工时间\"], \"eval_pipeline\": {\"竣工时间\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"国家\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"启动时间\": {\"preprocess\": [\"norm_date\"], \"metric\": [\"date_near\"]}, \"项目名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n31号、33号、41号公路升级改造项目和柬埔寨三条公路升级改造项目是同一个项目\\n国家可以是全称也可以是简称，如：印尼和印度尼西亚\"}, \"承包公司\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_086","query":"最近我是歌手的单依纯很火，歌手这个节目热度依旧。所以我想知道从2013年开始办歌手的节目，到2024年（包含2013年和2024年）的歌手，每一季每一期每位歌手唱了什么歌。你帮我以markdown的格式整一下吧。\n按照节目名称、节目期数、歌手名字、演唱歌曲作为表头，输出。其中节目名称需包含名字和季数，如：《我是歌手》第一季、《歌手·当打之年》第八季。节目期数格式为第x期，如：第一期、第十一期。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n节目名称\n节目期数\n歌手名字\n演唱歌曲。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"歌手名字\", \"演唱歌曲\"], \"required\": [\"节目名称\", \"节目期数\", \"歌手名字\", \"演唱歌曲\"], \"eval_pipeline\": {\"节目名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"节目期数\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"歌手名字\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"演唱歌曲\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_087","query":"我在研究中国居民近年来的收入与消费关系，需要一些数据支撑，请你帮我搜寻整理一份中国居民(城镇和农村)2000-2024年（包含2000年和2024年）的人均可支配收入、人均消费支出数据。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：年份、居民类别、人均可支配收入(元)、人均消费支出(元)。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"年份\", \"居民类别\"], \"required\": [\"年份\", \"居民类别\", \"人均可支配收入(元)\", \"人均消费支出(元)\"], \"eval_pipeline\": {\"人均可支配收入(元)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"人均消费支出(元)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"居民类别\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"zh"}
 {"instance_id":"ws_zh_088","query":"保定最大的汽车产业就是长城汽车了，所以我想看下截止到2025年5月31日长城汽车所有在售车型的信息，我希望获取其完整的产品线数据。统一车型可能存在多种配置版本请全部分行列出\n需要整理的具体信息包括（请按车型分类列出），查询不到或者没有的时候用“\/”代替：\n【车型名称：车型名称详细输出，同一系列下不同配置的名称都需分行列出，比如秦PLUS DM-i智驾版55KM领先型等】\n【车身尺寸 (mm)：长 x 宽 x 高】\n【最大扭矩 (N·m)：只抓取发动机的最大扭矩】\n【前悬架类型】\n【后悬架类型】\n。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n车型名称、车身尺寸、最大扭矩、前悬架类型、后悬架类型、辅助驾驶系统及功能。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"车型名称\"], \"required\": [\"车型名称\", \"车身尺寸\", \"最大扭矩\", \"前悬架类型\", \"后悬架类型\"], \"eval_pipeline\": {\"车型名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"车身尺寸\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"最大扭矩\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"前悬架类型\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"后悬架类型\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
-{"instance_id":"ws_zh_089","query":"帮我整理一下中国所有985高校的建校年份、主管部门、学校官网地址、以及这些学校在2025中国校友会排行榜的名次、2026 QS世界大学排名的名次。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：学校名称、建校年份、主管部门、学校官网地址、2025中国校友会排行榜名次、2026QS世界大学排名名次。\n其中：\n1. 学校名称需要写全称，例如：北京大学；\n2. 建校年份格式示例：1911年（以各学校官方信息为准）；\n3. 主管部门需要写全称，例如：中华人民共和国教育部；如果有多个主管部门，请用中文顿号隔开；\n4. 学校官网地址需要完整，例如：http:\/\/www.pku.edu.cn;\n5. 排行榜名次为阿拉伯数字，例如：11；\n6. 如排行榜中没有某个学校，则排名写“N\/A”。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"学校名称\"], \"required\": [\"学校名称\", \"建校年份\", \"主管部门\", \"学校官网地址\", \"2025中国校友会排行榜名次\", \"2026qs世界大学排名名次\"], \"eval_pipeline\": {\"建校年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"主管部门\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"2025中国校友会排行榜名次\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"2026qs世界大学排名名次\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"学校官网地址\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"学校名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"zh"}
-{"instance_id":"ws_zh_090","query":"我想调研一下截止到2025年6月30日北京地铁的运营情况，帮我汇总一下在这个时间之前，北京正式运营的线路名称、各个地铁线路总站数、总里程数、首次通车时间、起点站和终点站、运营机构。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：线路名称、总站数、总里程数、首次通车时间、运营机构、起点站和终点站。\n其中：\n线路名称格式示例：1号线、昌平线\n总站数为阿拉伯数字，例如：30、28\n总站数只需计算已开通的,不含施工封闭、暂缓、在建、待建\n总里程数单位为千米，不需要带单位，例如：52.7\n首次通车时间精确到日期即可，例如：1969年10月1日\n起点站和终点站只需说明现运行的，不含施工封闭、暂缓，在建、待建\n起点站和终点站需要写完整的站名，之间用顿号分开，例如：xx站、xxx站。\n运营机构需要写详细的全称，例如：北京市地铁运营有限公司第二分公司\n不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"线路名称\"], \"required\": [\"线路名称\", \"总站数\", \"总里程数\", \"首次通车时间\", \"运营机构\", \"起点站和终点站\"], \"eval_pipeline\": {\"总站数\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"起点站和终点站\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"总里程数\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.01}, \"首次通车时间\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"运营机构\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"线路名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"zh"}
+{"instance_id":"ws_zh_089","query":"帮我整理一下中国所有985高校的建校年份(如果该学校存在前身，则按照前身学校的建校时间，如果该学校由多所学校融合创办则按照其中办学最早的学校来算)、主管部门、学校官网地址、以及这些学校在2025中国校友会排行榜的名次、2026 QS世界大学排名的名次。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：学校名称、建校年份、主管部门、学校官网地址、2025中国校友会排行榜名次、2026QS世界大学排名名次。\n其中：\n1. 学校名称需要写全称，例如：北京大学；\n2. 建校年份格式示例：1911年（以各学校官方信息为准）；\n3. 主管部门需要写全称，例如：中华人民共和国教育部；如果有多个主管部门，请用中文顿号隔开；\n4. 学校官网地址需要完整，例如：http:\/\/www.pku.edu.cn;\n5. 排行榜名次为阿拉伯数字，例如：11；\n6. 如排行榜中没有某个学校，则排名写“N\/A”。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"学校名称\"], \"required\": [\"学校名称\", \"建校年份\", \"主管部门\", \"学校官网地址\", \"2025中国校友会排行榜名次\", \"2026qs世界大学排名名次\"], \"eval_pipeline\": {\"建校年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"2025中国校友会排行榜名次\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"2026qs世界大学排名名次\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"学校官网地址\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"主管部门\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一��即可，不需要字字对应。\"}, \"学校名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"zh"}
+{"instance_id":"ws_zh_090","query":"我想调研一下截止到2025年6月30日北京地铁的运营情况，帮我汇总一下在这个时间之前，北京正式运营的地铁线路名称、各个地铁线路总站数、总里程数、首次通车时间、起点站和终点站、运营机构。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：线路名称、总站数、总里程数、首次通车时间、运营机构、起点站和终点站。\n其中：\n线路名称格式示例：1号线、昌平线\n总站数为阿拉伯数字，例如：30、28\n总站数只需计算已开通的,不含施工封闭、暂缓、在建、待建\n总里程数单位为千米，不需要带单位，例如：52.7\n首次通车时间精确到日期即可，例如：1969年10月1日\n起点站和终点站只需说明现运行的，不含施工封闭、暂缓，在建、待建\n起点站和终点站需要写完整的站名，之间用顿号分开，例如：xx站、xxx站。\n运营机构需要写详细的全称，例如：北京市地铁运营有限公司第二分公司\n不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"线路名称\"], \"required\": [\"线路名称\", \"总站数\", \"总里程数\", \"首次通车时间\", \"运营机构\", \"起点站和终点站\"], \"eval_pipeline\": {\"总站数\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"起点站和终点站\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"总里程数\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"首次通车时间\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"运营机构\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"线路名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"zh"}
 {"instance_id":"ws_zh_091","query":"这么近那么美，周末到河北。这个口号听好几年，但是实在是不知道河北有啥可玩的，所以请你帮我整理一下截止到2024年年底石家庄、承德、秦皇岛三个城市的4a、5a景区以及相关信息。\n你需要输出以下信息\n景区名称\n景区等级 (如：5A, 4A)\n所在城市\n具体地址。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n景区名称\n景区等级\n所在城市\n具体地址。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"景区名称\"], \"required\": [\"景区名称\", \"景区等级\", \"所在城市\", \"具体地址\"], \"eval_pipeline\": {\"景区名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"景区等级\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"所在城市\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"具体地址\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_092","query":"帮我总结一下有史以来至2024年年底同时获得中国金鹰奖、白玉兰奖最佳电视剧奖提名的电视剧，汇总一下这些电视剧的首播时间、导演和集数。集数以备案数为准。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：电视剧名称、首播时间、导演和集数。\n其中：\n电视剧名称需要填写全称，不包含书名号，例如：漫长的季节；\n首播时间精确到日期即可，格式示例：2023年1月15日；\n导演如果有多个，则姓名用中文逗号分隔，例如：张艺谋、陈凯歌；\n集数填写阿拉伯数字，不需要带单位，例如：20。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"电视剧名称\"], \"required\": [\"电视剧名称\", \"首播时间\", \"导演\", \"集数\"], \"eval_pipeline\": {\"集数\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"电视剧名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"首播时间\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"导演\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n导演只答出参考答案的子集也算对\"}}}","language":"zh"}
 {"instance_id":"ws_zh_093","query":"孩子马上要上高中了，正在择校。帮忙汇总一下截止到2024年底海淀区所有公办高中的相关信息，包括学校的名称、创办时间、校训、详细地址。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：学校名称、创办时间、校训、详细地址。\n其中：\n学校名称需要填写全称，例如：中国人民大学附属中学；\n创办时间精确到年份即可，例如：1950年；\n校训如果没有则填写无；\n地址需要写的详细，例如：北京市海淀区中关村大街37号；。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"学校名称\"], \"required\": [\"学校名称\", \"创办时间\", \"校训\", \"详细地址\"], \"eval_pipeline\": {\"学校名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"创办时间\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"校训\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"详细地址\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_094","query":"都说中国是\"基建狂魔\"，以通信运营业网络基础设施建设为切口，请你帮我整理一份2015-2024年来（包含2015年和2024年），中国光纤铺设与移动网络基站数量的变化情况。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：年份、网络基础设施类别、新增数量\/长度(万个\/万公里)、总计数量\/长度(万个\/万公里)\n注：\n1. 网络基础设施类别包括移动通信基站、4G基站数、5G基站数、光缆线路，无法统计的内容使用\"\/\"代替；\n2. 数量的单位统一为\"万个\"，长度的单位统一为\"万公里\"。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"年份\", \"网络基础设施类别\"], \"required\": [\"年份\", \"网络基础设施类别\", \"新增数量\/长度(万个\/万公里)\", \"总计数量\/长度(万个\/万公里)\"], \"eval_pipeline\": {\"新增数量\/长度(万个\/万公里)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"总计数量\/长度(万个\/万公里)\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"网络基础设施类别\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"zh"}
 {"instance_id":"ws_zh_095","query":"请帮我整理出郎平担任中国国家女子排球队主教练时（注意有两个任期：2013年4月 - 2016年8月；2017年4月 - 2021年8月），所有重要国际赛事的成绩与信息。请注意，只整理她作为主教练的赛事。\n这里的重要国际赛事包括：奥运会、世界女排锦标赛、女排世界杯、世界女排联赛（VNL）、亚洲排球锦标赛、亚洲杯、大冠军杯、瑞士女排精英赛、世界女排大奖赛、亚运会\n需要包含以下信息：年份、赛事名称、上场中国女排队员（名字即可，不需要具体的前中后卫信息，名字之间以顿号连接）、赛事最终结果\/名次。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n年份、赛事名称、上场中国女排队员、赛事最终结果\/名次。\n赛事名称请完整输出例如：2016xxxxx赛。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"年份\", \"赛事名称\"], \"required\": [\"年份\", \"赛事名称\", \"上场中国女排队员\", \"赛事最终结果\/名次\"], \"eval_pipeline\": {\"年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"赛事名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"上场中国女排队员\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"赛事最终结果\/名次\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
-{"instance_id":"ws_zh_096","query":"梁朝伟是众所周知的影帝，请你帮我整理下他出道以来所有担任主角的且已上映的电影。时间跨度为：1985年-2024年（包含1985年和2024年）。\n整理的信息要包括：电影名称、导演、上映年份（中国）、发行方、梁朝伟通过该电影获取的专属奖项，例如最佳男主角(提名不算)。如果有些信息无法检索到，请输出\"-\"。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n电影名称、导演、上映年份、发行方、梁朝伟通过该电影所获奖项。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"电影名称\"], \"required\": [\"电影名称\", \"导演\", \"上映年份\", \"发行方\", \"梁朝伟通过该电影所获奖项\"], \"eval_pipeline\": {\"电影名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"导演\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"发行方\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"梁朝伟通过该电影所获奖项\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"上映年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
+{"instance_id":"ws_zh_096","query":"梁朝伟是众所周知的影帝，请你帮我整理下他出道以来所有出演的且已上映的电影（纪录片、访谈、多段式短片电影和进行配音的电影不算）。时间跨度为：2000年初-2023年末\n整理的信息要包括：电影名称、导演、首次上映年份(中国)、梁朝伟通过该电影获取的专属奖项，例如最佳男主角、最佳男配角(提名不算)。如果有些信息无法检索到，请输出\"-\"。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n电影名称、导演、上映年份、梁朝伟通过该电影所获奖项。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"电影名称\"], \"required\": [\"电影名称\", \"导演\", \"上映年份\", \"梁朝伟通过该电影所获奖项\"], \"eval_pipeline\": {\"电影名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"导演\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"梁朝伟通过该电影所获奖项\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应���\"}, \"上映年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}}}","language":"zh"}
 {"instance_id":"ws_zh_097","query":"在研究中国一线城市的人口流动趋势，以2024年的一线城市名单为准(包含新一线城市)，请你帮我以表格的形式搜索整理一些数据，时间跨度为2020-2023年（包含2020年和2023年），具体字段包括：年份、城市、净流入\/流出人口数、出生人口数、死亡人口数\n负数代表净流出人口数，正数代表净流入人口数。涉及到人口数的单位均以万计算，精确到小数点后一位，如23.5万。对于搜索不到的数据，请用NA标记。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：年份、城市、净流入人口数或净流出人口数（万人）、出生人口数（万人）、死亡人口数（万人）。\n出生人口数和死亡人口数以对应省市统计局的口径为准。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"年份\", \"城市\"], \"required\": [\"年份\", \"城市\", \"净流入人口数或净流出人口数（万人）\", \"出生人口数（万人）\", \"死亡人口数（万人）\"], \"eval_pipeline\": {\"净流入人口数或净流出人口数（万人）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"出生人口数（万人）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"死亡人口数（万人）\": {\"preprocess\": [\"extract_number\"], \"metric\": [\"number_near\"], \"criterion\": 0.05}, \"年份\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}, \"城市\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"zh"}
 {"instance_id":"ws_zh_098","query":"截止到2025年5月31日，给我整理所有体育学北大核心期刊各自最新一刊所有研究论文，包括期刊名称、论文名称、论文关键词、论文作者。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：\n期刊名称、论文名称、论文关键词、论文作者。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"论文名称\"], \"required\": [\"期刊名称\", \"论文名称\", \"论文关键词\", \"论文作者\"], \"eval_pipeline\": {\"期刊名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"论文作者\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"论文关键词\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"论文名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"zh"}
 {"instance_id":"ws_zh_099","query":"我在北京上班，想每个周末都去周边城市玩一下。帮我汇总一下从北京出发高铁2个小时内可达的城市中有哪些5A级景区，给出这些景区的名称、所在城市、门票价格（只给出旺季成人全价普通门票即可）以及是否属于世界文化和自然遗产。请以一整个Markdown表格的格式输出整理后的数据，不要拆分成多个markdown表格，每个单元格都需要按列名要求输出，不得无故省略，输出采用中文。\n表格中的列名依次为：景区名称、所在城市、旺季成人全价门票价格。\n其中：\n景区名称请填写全称，例如：避暑山庄。\n所在城市请填写全称，例如：承德市。\n旺季成人全价门票价格单位为元，填写整数即可，不需要带单位，例如：120；如果该景点免费，则填写0。注意，只填写景区门票价格即可，不要写联票价格。\n是否属于世界文化和自然遗产，只能填“是”或“否”。不要问我任何问题，只需输出结果，输出格式为```markdown{数据内容}```","evaluation":"{\"unique_columns\": [\"景区名称\"], \"required\": [\"景区名称\", \"所在城市\", \"旺季成人全价门票价格\", \"是否属于世界文化和自然遗产\"], \"eval_pipeline\": {\"所在城市\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可��不需要字字对应。\"}, \"旺季成人全价门票价格\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\\n只答出标准答案中的子集可算对\"}, \"是否属于世界文化和自然遗产\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"llm_judge\"], \"criterion\": \"和参考答案语义相同大致、或者指向的实体一致即可，不需要字字对应。\"}, \"景区名称\": {\"preprocess\": [\"norm_str\"], \"metric\": [\"exact_match\"]}}}","language":"zh"}