Question:
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.

Please organize the results in one Markdown table with the following columns:
Subject, 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
Please use the universities’ full official names in English.
Use only Arabic numerals in the ranking, for example: 1.
Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is
```markdown
{data_content}
```.

{"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"]}}}


Question:
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.
The scope covers only their standard product in the Core / Permanent Range excluding any flavor variants or limited-edition/seasonal releases.
Please 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 '/'.
Please ensure that no cells in the “Brand” column are left blank.

Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

{"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."}}}


Question:
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.

Please organize the results in one Markdown table with columns labeled: Date, Event Name, Level, Discipline, Result, Top 3 Players.

Don't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

{"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."}}}


Question:
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.

Notes:
Market Restriction: Only include models officially released in the U.S. market (exclude region-exclusive models limited to specific countries/regions).
Base 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.
4G/5G Preference: If a base model has both 4G and 5G versions, only include the 5G version.
Release Date: The time when it was first released in the U.S..
Formatting Rules:
Release Date: yyyy-mm-dd
Pre-installed OS: Android/iOS number (e.g., Android 5)
Resolution: "numberxnumber" (e.g., 2340x1080)
CPU Manufacturing Process: "number+unit" (e.g., 4nm)
CPU Core Count: "x+core" (e.g., 8-core)
Highest Camera Resolution: "number+unit" (e.g., 200MP)
CPU Model: Omit the processor brand (e.g., only "Snapdragon 8 Gen 2" instead of "Qualcomm Snapdragon 8 Gen 2")
storage: refer to the lowest storage of the base model.

Please organize the results in one Markdown table with the specified column order:
Model 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)

Don't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

{"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."}}}


Question:
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?

Please organize the results in one Markdown table with the following columns: Country, University, Alliance, and Minimum GPA Requirement.

Note:
The value of “Alliance” can only be “Ivy League” or “Group of Eight”
If no specified minimum GPA cutoff in official resources, list as "no minimum GPA specified"

Don't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

{"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."}}}


Question:
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.

Please organize the results in one Markdown table with the following columns:
Date, the Concert’s English Name, Host Country, Host City, Host Venue

Notes: Do not use date ranges for Date, list it in the format of “Day Month, Year”, for example: 4th June, 2011
Don't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

{"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."}}}


Question:
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.

Please 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.
Launch 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 /.

Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

{"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."}}}


Question:
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?

 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
For the statistical month, keep the format as January 2025.
For total trading value and domestic market capitalization, both of the terms refer to equity.
Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```

{"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"]}}}


Question:
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?

Please organize the results in one Markdown table with the following columns:
Program Name, Length, Annual International Tuition Fees, IELTS Score Requirement, Compulsory Module Titles, Credits.
Note:
The meaning of 'length' is the duration of this master's program.
For any missing information, fill in '/'.

Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

{"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."}}}


Question:
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.

Please organize the results in one Markdown table with the following columns:
Designation Category, Area Name, Regions, Designation Time, Responsible Public Body, Counties, Website

Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

{"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."}}}


Question:
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.

Please organize the results in one Markdown table with the following columns:
Award 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.
If 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.
Separate multiple fuel types by comma.
Only use Arabic numerals for the columns Year, Passengers, Doors, Overall Ratings 2025, Quality & Reliability Score 2025, Driving Experience Score 2025, Resale Value Score 2025.
Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```

{"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"]}}}


Question:
Please compile a table listing the “CNN Hero of the Year” for every year the award was actually presented, from its first introduction through 2024 (including 2024), along with relevant details for each honoree.

Please organize the results in one Markdown table with the following columns:
Year of Award, Full Name, Gender, Date of Birth, Country of Birth,  University, Devoted Country, Background/Occupation, Major Contributions

Notes:
- Skip the year when no single CNN Hero of the Year is honored.
- For any field where information is unavailable or not applicable, please enter "NA" in the corresponding cell.
- Please write the date of birth in the format "Mon DD, yyyy" (e.g., Feb 01, 1994). If the specific day is missing, simply use "-, yyyy" (e.g., -, 1994).
- You can use the following standard values for gender:
• Male
• Female
• Non-binary
- If the honoree did not attend college, write “NA” in the University column.
- In the “Background/Occupation” column, specify the primary profession or social role, such as a nurse, teacher, or social entrepreneur.
- In the “Major Contribution” column, briefly describe their main contributions to public welfare or society, for example providing education for children, aiding the homeless, or promoting environmental protection.
- If you know the country where the individual engaged, you may note this in the “Devoted Country” column, for instance “China,” “United States,” or “India.”
Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```

{"unique_columns": ["yearofaward"], "required": ["yearofaward", "fullname", "gender", "dateofbirth", "countryofbirth", "university", "devotedcountry", "background/occupation", "majorcontributions"], "eval_pipeline": {"yearofaward": {"preprocess": ["norm_str"], "metric": ["exact_match"]}, "gender": {"preprocess": ["norm_str"], "metric": ["exact_match"]}, "dateofbirth": {"preprocess": ["norm_date"], "metric": ["date_near"]}, "fullname": {"preprocess": ["norm_str"], "metric": ["llm_judge"], "criterion": "It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence."}, "countryofbirth": {"preprocess": ["norm_str"], "metric": ["llm_judge"], "criterion": "It is sufficient if the semantics are approximately 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.\nIt is sufficient as long as the answer to be evaluated refers to the same university in the  reference answer . Supplementary information (e.g., major) does not need to be consistent."}, "devotedcountry": {"preprocess": ["norm_str"], "metric": ["llm_judge"], "criterion": "It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence."}, "background/occupation": {"preprocess": ["norm_str"], "metric": ["llm_judge"], "criterion": "It is sufficient if the semantics are approximately 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 to-be-evaluated answer has similar semantics to the reference answer, and the expressions regarding background/occupation are approximate"}, "majorcontributions": {"preprocess": ["norm_str"], "metric": ["llm_judge"], "criterion": "It is sufficient if the semantics are approximately 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 to-be-evaluated answer has similar semantics to the reference answer"}}}


Question:
I’m a huge fan of Lana Del Rey, can you list all the songs of her official studio albums (from Born to Die to Did You Know That There's a Tunnel Under Ocean Blvd) and release years? For albums, please search the standard edition.

 Please organize the results in one Markdown table with the following column names in order:
album, release year, song

Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

{"unique_columns": ["song"], "required": ["album", "releaseyear", "song"], "eval_pipeline": {"album": {"preprocess": ["norm_str"], "metric": ["exact_match"]}, "releaseyear": {"preprocess": ["norm_str"], "metric": ["exact_match"]}, "song": {"preprocess": ["norm_str"], "metric": ["exact_match"]}}}


Question:
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).

 Please organize the results in one Markdown table with the following column names in order:
Year, Airport, Rank, Location, Total Passengers, Code(IATA/ICAO)

Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

{"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."}}}


Question:
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.

Please output the organized data in the format of one Markdown table.
The column names in the table are as follows in sequence:
Rank, Company Name, Number of Managed Units in 2024, Company Establishment Time.

Don't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

{"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."}}}


Question:
Michael Phelps is one of the most decorated Olympians in history.
I am interested in compiling his medal achievements of individual events from major international competitions, specifically the Olympic Games, and the World Aquatics Championships.

Please 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.

Please organize the results in one Markdown table with the following columns:
Date, Location, Meet, Event, Time, Standing, Gold, Silver, Bronze
Notes:
Please include the city that held the final competition under Location, in the format of "City, country", for example: Beijing, China.
If 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.
Time is accurate to 1/100 second. Output format example: 1:23.01
Write the full name or abbreviation of the event, for example, 200m butterfly or 200m fly.
If there is content that cannot be found, use "-" instead.

Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```

{"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."}}}


Question:
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.
1. Pearl Harbor National Memorial
2. Muir Woods National Monument
3. Mount Rushmore National Memorial
4. Arches National Park
5. White Sands National Park
6. Zion National Park
7. Yellowstone National Park
8. Acadia National Park
9. Crater Lake National Park
10. Haleakala National Park
11. Great Smoky Mountains National Park
12. Redwood National and State Parks
13. Canyonlands National Park

Please 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.

Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

{"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"]}}}


Question:
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).

Please organize the results in one Markdown table with the following column names in order:
Fiscal Year, Federal Budget, Federal Spending, Federal Deficit, National Debt, Net Interest Cost.

Under the Fiscal Year, state the statistics like FY2015, FY2016.

Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```

{"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}}}


Question:
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.

Please output the organized data in the format of one Markdown table.
The column names in the table are as follows in sequence:
Company 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)

Don't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

{"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."}}}


Question:
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".

Please output the organized data in a single Markdown table. The column names in the table should be as follows, in order:
Country, Name of the Heritage Site, Type of Heritage, Location, Area, Year of Recognition

Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```

{"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."}}}


Question:
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.

Please organize the results in one Markdown table with the following columns:
Restaurant, Main Cuisine Style, Address
Don't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```

{"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."}}}


Question:
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.

Please organize the results in one Markdown table with the following columns:
Category,Rank, Title,Singer,Language,Songwriter(s),Producer(s),Release Date.
Note:
1.The song rankings need to be sorted in descending order.
2. Most-streamed songs globally and U.S. most-streamed songs need to be distinguished in the "Category" column.
3. Release date should be written as YYYY/MM/DD
4. If there is more than one songwriter or producer, separate them by comma.

Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

{"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\"."}}}


Question:
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).

Please organize the results in one Markdown table with the following columns:
Journal, Issued Date, Article Title, Author(s), Volume Number, Issue Number, Page Number(s).

Notes:
Record the full name of the author(s), and if there is more than one author, separate each name by semicolon.
If the issued date, volume number, issue number, or page number is not applicable, leave the cell blank.
For page number, format it as xxx-xxx.
Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```

{"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."}}}


Question:
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.

Please give me the organized data in the format of one Markdown table, with the column names as:
Year, Champion, Host Stadium, Host City,  Nielsen HHLD Rating, Viewership (million), Half-time Show Sponsors.
For host city, give the full name of the city, like Houston.

Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

{"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."}}}


Question:
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.

Please organize the results in one Markdown table with the following column names in order:
Clinic, County, Full Address, Phone Number, Saturday Operation Hours
List the phone number in the format of (xxx) xxx-xxxx
Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```

{"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"]}}}


Question:
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.

Please 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)

Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

For example,
```markdown
| State           | Number of Congressional Districts | Number of Electors | Governor (Party)     | Senator 1 (Party)& Senator 2 (Party) |
|-----------------|-----------------------------------|--------------------|----------------------|-------------------|-------------------|
| Alabama         | 7                                 | 9                  | Kay Ivey (R)         | Tommy Tuberville (R)& Katie Britt (R)   |

{"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."}}}


Question:
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.

Please present the information in one Markdown table with these column headers: Museum, Last Entry Time, Ticket Price (Pounds), and Address.
Format the "Last Entry Time" like "4:00 pm".

Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

{"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."}}}


Question:
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".

Please present the organized data in one Markdown table format.
The  columns  names are as follows:
Brand, Worldwide, New York, Detroit, Seattle

Don't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

{"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}}}


Question:
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.

 Please organize the results in one Markdown table with the specified column order:
Movie, Release Date, US Box Office (dollar), Worldwide Box office(dollar)
Don't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```

{"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}}}


Question:
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.

Please organize the results in one Markdown table with the following columns:
Case Name, Case Date, Docket Number, Opinion Author, Judgment Outcome

Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

{"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."}}}


Question:
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.

Please output the organized data in the format of one Markdown table.
The table's columns, in order, are:
Actress, Year of Release, Title of Film, Character Name, Awards.
Notes: if the film didn't win any awards, please fill in NA.

Don't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

{"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."}}}


Question:
I am doing research on public transportation in Sydney. I wanna know each Sydney train's line number, line name, total number of stations, and termini. Pay attention: each Sydney railway line, that has been put into use by May 31, 2025, is included. The total number of stations should reflect the status quo in Sydney as of May 2025.

Please organize the results in one Markdown table with the following columns:
Line Number, Line Name, Total Stations, Termini
Note: When compiling the total number of stations, variations in station count resulting from peak-hour additions or reductions are excluded.

Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

{"unique_columns": ["linenumber"], "required": ["linenumber", "linename", "totalstations", "termini"], "eval_pipeline": {"linenumber": {"preprocess": ["norm_str"], "metric": ["exact_match"]}, "totalstations": {"preprocess": ["norm_str"], "metric": ["exact_match"]}, "linename": {"preprocess": ["norm_str"], "metric": ["llm_judge"], "criterion": "It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence."}, "termini": {"preprocess": ["norm_str"], "metric": ["llm_judge"], "criterion": "It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence."}}}


Question:
Please organize the following data for Apple, Amazon, Google, Microsoft, and Netflix over the five-year period from 2020 to 2024: Total revenue (in billions of US dollars), Operating profit according to International Financial Reporting Standards (in billions of US dollars), Operating profit margin, and Free cash flow (FCF, in billions of US dollars). Please quote the data from the financial reports of the company. Mark the data that cannot be found with "-". The value should be specified to two decimal places

Please output the organized data in one Markdown table format.
The column names in the table are as follows in sequence:
Company, Year, Total Revenue(Billion), Operating Profit(Billion), Operating Profit Margin, FCF(Billion)

Don't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

{"unique_columns": ["company", "year"], "required": ["company", "year", "totalrevenue(billion)", "operatingprofit(billion)", "operatingprofitmargin", "fcf(billion)"], "eval_pipeline": {"totalrevenue(billion)": {"preprocess": ["extract_number"], "metric": ["number_near"], "criterion": 0.05}, "operatingprofit(billion)": {"preprocess": ["extract_number"], "metric": ["number_near"], "criterion": 0.05}, "operatingprofitmargin": {"preprocess": ["extract_number"], "metric": ["number_near"], "criterion": 0.05}, "fcf(billion)": {"preprocess": ["extract_number"], "metric": ["number_near"], "criterion": 0.05}, "company": {"preprocess": ["norm_str"], "metric": ["exact_match"]}, "year": {"preprocess": ["norm_str"], "metric": ["exact_match"]}}}


Question:
Please track and compile the average monthly house prices for England, Scotland, Wales and Northern Ireland, for the period from January 2022 to April 2025 (including January 2022 and April 2025). Please cite all the statistics from government websites.

Please output the compiled data in one Markdown table format. The column names in the table should be, in order: Month, House Price in England (Pounds), House Price in Wales (Pounds), House Price in Scotland (Pounds), House Price in Northern Ireland (Pounds)
For the column 'Month', list the month in the format 2022/01, 2022/02.

Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```

{"unique_columns": ["month"], "required": ["month", "housepriceinengland(pounds)", "housepriceinwales(pounds)", "housepriceinscotland(pounds)", "housepriceinnorthernireland(pounds)"], "eval_pipeline": {"housepriceinengland(pounds)": {"preprocess": ["extract_number"], "metric": ["number_near"], "criterion": 0.05}, "housepriceinwales(pounds)": {"preprocess": ["extract_number"], "metric": ["number_near"], "criterion": 0.05}, "housepriceinscotland(pounds)": {"preprocess": ["extract_number"], "metric": ["number_near"], "criterion": 0.05}, "housepriceinnorthernireland(pounds)": {"preprocess": ["extract_number"], "metric": ["number_near"], "criterion": 0.05}, "month": {"preprocess": ["norm_str"], "metric": ["exact_match"]}}}


Question:
I want to compare the labor market between four southern US states, namely Alabama, Georgia, Louisiana, and Mississippi. I need to compile monthly employment data not seasonally adjusted for these states from January 2024 to June 2024 (including January 2024 and June 2024). Please provide the following:
Unadjusted Unemployment Rate
Labor Force Participation Rate
All Employees – Total Nonfarm (Thousands of Persons)
All Employees – Manufacturing (Thousands of Persons)
Average Hourly Earnings of All Employees (Total Private)
Average Hourly Earnings of All Employees (Manufacturing)

Please output the organized data in Markdown table format.
The column names in the table should be, in order:
States
Statistical Month
Unadjusted Unemployment Rate(%)
Labor Force Participation Rate(%)
All Employees – Total Nonfarm (in thousands)
All Employees – Manufacturing (in thousands)
Average Hourly Earnings of All Employees (Total Private)
Average Hourly Earnings of All Employees (Manufacturing)
For Statistical Month, use the format yyyy-mm, like 2024-06.

Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

{"unique_columns": ["states", "statisticalmonth"], "required": ["states", "statisticalmonth", "unadjustedunemploymentrate(%)", "laborforceparticipationrate(%)", "allemployees–totalnonfarm(inthousands)", "allemployees–manufacturing(inthousands)", "averagehourlyearningsofallemployees(totalprivate)", "averagehourlyearningsofallemployees(manufacturing)"], "eval_pipeline": {"states": {"preprocess": ["norm_str"], "metric": ["exact_match"]}, "unadjustedunemploymentrate(%)": {"preprocess": ["extract_number"], "metric": ["number_near"], "criterion": 0.05}, "laborforceparticipationrate(%)": {"preprocess": ["extract_number"], "metric": ["number_near"], "criterion": 0.05}, "allemployees–totalnonfarm(inthousands)": {"preprocess": ["extract_number"], "metric": ["number_near"], "criterion": 0.05}, "allemployees–manufacturing(inthousands)": {"preprocess": ["extract_number"], "metric": ["number_near"], "criterion": 0.05}, "averagehourlyearningsofallemployees(totalprivate)": {"preprocess": ["extract_number"], "metric": ["number_near"], "criterion": 0.05}, "averagehourlyearningsofallemployees(manufacturing)": {"preprocess": ["extract_number"], "metric": ["number_near"], "criterion": 0.05}, "statisticalmonth": {"preprocess": ["norm_str"], "metric": ["llm_judge"], "criterion": "It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence."}}}


Question:
I'm seeking permanent government job opportunities with the Department of Justice.
Please check USAJOBS for any publicly available Attorney positions that list an open date between January 1st 2025 and June 25th 2025. Then, organize the information of position title, the hiring agency, position open date, position closing date, the duty location, and the salary range.

Please output the organized data in the format of one Markdown table.
The column names are as follows in sequence:
Job Position, Hiring Agency, Open Date, Closing Date, Duty Location, Salary (Dollars), Control number
If the salary or closing date is not publicly available, use “-”.  Separate multiple duty location by semicolon; if the locations are not specified, list it as "unknown".
List the open date and closing date in the format dd/mm/yy.

Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

{"unique_columns": ["controlnumber"], "required": ["jobposition", "hiringagency", "opendate", "closingdate", "dutylocation", "salary(dollars)", "controlnumber"], "eval_pipeline": {"jobposition": {"preprocess": ["norm_str"], "metric": ["exact_match"]}, "opendate": {"preprocess": ["norm_str"], "metric": ["exact_match"]}, "closingdate": {"preprocess": ["norm_str"], "metric": ["exact_match"]}, "controlnumber": {"preprocess": ["norm_str"], "metric": ["exact_match"]}, "hiringagency": {"preprocess": ["norm_str"], "metric": ["llm_judge"], "criterion": "It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence."}, "dutylocation": {"preprocess": ["norm_str"], "metric": ["llm_judge"], "criterion": "It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence."}, "salary(dollars)": {"preprocess": ["norm_str"], "metric": ["llm_judge"], "criterion": "It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence."}}}


Question:
I'm a huge 'Love is Blind' fan and want to know which couples got married in seasons 1-7. Please list them as '[Man's Name] & [Woman's Name]'. Could you also find out how many of those couples are still together or already divorced as of the end of June 2025?
If more than one couple wed in the same season, put each one on its own line.

Please organize the results in one Markdown table with the following column names in order:
Season, Couple married at the show, Status
As for the Status column, just give the information of "Still Married", or "Divorced"

Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

{"unique_columns": ["couplemarriedattheshow"], "required": ["season", "couplemarriedattheshow", "status"], "eval_pipeline": {"status": {"preprocess": ["norm_str"], "metric": ["exact_match"]}, "couplemarriedattheshow": {"preprocess": ["norm_str"], "metric": ["llm_judge"], "criterion": "It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence."}, "season": {"preprocess": ["norm_str"], "metric": ["llm_judge"], "criterion": "It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence."}}}


Question:
Summarize all smartphones released by Google between January 2022 and July 2025 (including January 2022 and July 2025). Include the following details: device name, available colors, announced date, RAM, U.S. launch price ($), materials, size, OS, battery capacity.

Present the data in one Markdown table with columns: Device, Available colors, Announced date, RAM, U.S. launch price (MSRP by storage), Materials (frame / front & back), Size (H × W × D; mm), Launch OS, Battery capacity.

Notes:
Specify different prices for varying storage tiers using Google’s initial MSRP in USD. And please list all available colors per model. For foldable phones, the dimensions column must include two specifications: unfolded dimensions and folded dimensions.

Formatting Rules:
Available colors: separated by commas
Announced date: yyyy-mm-dd
RAM: number GB, e.g., 2 GB
U.S. launch price: If there are more than one storage editions, use comma to separate them, such as 128 GB: 300, 256 GB: 500.
Materials: frame's material / material A front, B back. e.g., steel / GG Victus 2 front, metal back. If the material of front and back is the same one, you can write as  metal / glass front & back.
Battery capacity: xxx mAh.

Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

{"unique_columns": ["device"], "required": ["device", "availablecolors", "announceddate", "ram", "u.s.launchprice(msrpbystorage)", "materials(frame/front&back)", "size(h×w×d;mm)", "launchos", "batterycapacity"], "eval_pipeline": {"announceddate": {"preprocess": ["norm_str"], "metric": ["exact_match"]}, "ram": {"preprocess": ["norm_str"], "metric": ["exact_match"]}, "batterycapacity": {"preprocess": ["norm_str"], "metric": ["exact_match"]}, "device": {"preprocess": ["norm_str"], "metric": ["llm_judge"], "criterion": "It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence."}, "availablecolors": {"preprocess": ["norm_str"], "metric": ["llm_judge"], "criterion": "It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence."}, "u.s.launchprice(msrpbystorage)": {"preprocess": ["norm_str"], "metric": ["llm_judge"], "criterion": "It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence."}, "materials(frame/front&back)": {"preprocess": ["norm_str"], "metric": ["llm_judge"], "criterion": "It is sufficient if the semantics are approximately 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 response to be evaluated is acceptable if broader than the reference answer, e.g., answering \"glass\" for \"Corning Gorilla Glass\" is considered as correct"}, "size(h×w×d;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."}, "launchos": {"preprocess": ["norm_str"], "metric": ["llm_judge"], "criterion": "It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence."}}}


Question:
Please provide a list of the top 50 universities in the QS 2026 World University Rankings, including each university’s rank, university name, founding year, country, and specific address of its main campus.

Please organize the results in one Markdown table with the following columns:
rank, university name, founding year, country, specific address

Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

{"unique_columns": ["universityname"], "required": ["rank", "universityname", "country", "specificaddress"], "eval_pipeline": {"rank": {"preprocess": ["norm_str"], "metric": ["exact_match"]}, "specificaddress": {"preprocess": ["norm_str"], "metric": ["llm_judge"], "criterion": "It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence."}, "universityname": {"preprocess": ["norm_str"], "metric": ["llm_judge"], "criterion": "It is sufficient if the semantics are approximately 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."}}}


Question:
I'd like to get a list of the top 10 apps downloaded worldwide each year from 2020 to 2022. For each app, I need to know its category (e.g., game, health app, etc.), the company that created it, the country of origin of the company, and the download volume (in millions).

Please organize the results in one Markdown table with the following columns names in order:
Year, Rank, App, Downloads(million), Category, Parent Company, Country

Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

{"unique_columns": ["year", "rank"], "required": ["year", "rank", "app", "downloads(million)", "category", "parentcompany", "country"], "eval_pipeline": {"year": {"preprocess": ["norm_str"], "metric": ["exact_match"]}, "rank": {"preprocess": ["norm_str"], "metric": ["exact_match"]}, "downloads(million)": {"preprocess": ["extract_number"], "metric": ["number_near"], "criterion": 0.05}, "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."}, "parentcompany": {"preprocess": ["norm_str"], "metric": ["llm_judge"], "criterion": "It is sufficient if the semantics are approximately 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."}, "app": {"preprocess": ["norm_str"], "metric": ["llm_judge"], "criterion": "It is sufficient if the semantics are approximately the same as the reference answer or if they point to the same entity. There is no need for a word-for-word correspondence."}}}


Question:
I’ve been in a bit of a reading slump and want to find some new books. Could you please compile a list of the winners of all categories from the Annual Goodreads Choice Awards for each year from 2022 to 2024 (including 2024), as well as the top ten best sellers on Amazon for those same years? Also, please include each author’s name and nationality, and the genre of the book (select from Fiction/Non-fiction).

Please output the organized data in Markdown table format.
The column names are as follows in sequence: Award, Award Year, Genre, Book Title, Author, Nationality
Note:
Only list one book per row;
In the "Award" column, you need to distinguish between the "Annual Goodreads Choice Awards" and the "Amazon Best Sellers in Books".

Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

{"unique_columns": ["award", "awardyear", "booktitle"], "required": ["award", "awardyear", "genre", "booktitle", "author", "nationality"], "eval_pipeline": {"award": {"preprocess": ["norm_str"], "metric": ["exact_match"]}, "awardyear": {"preprocess": ["norm_str"], "metric": ["exact_match"]}, "booktitle": {"preprocess": ["norm_str"], "metric": ["exact_match"]}, "nationality": {"preprocess": ["norm_str"], "metric": ["exact_match"]}, "genre": {"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."}}}


Question:
Please compare the growth trajectory of the leading global e-commerce platforms by compiling their quarterly performance for 2024—Amazon, eBay, Shopee (Sea), Alibaba. Use figures from official financial reports for publicly traded companies; for privately held firms, cite reputable news sources and mark relevant numbers with an asterisk (*) to denote that they are estimates. For information you can't find online, just fill in with "-".

Present the data in one Markdown table with the columns (in this order): Company, Quarter, GMV (USD bn), Revenue (USD bn), Net Profit (USD bn).

For Quarters,  label it simply Q1, Q2, etc.

Don't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

{"unique_columns": ["company", "quarter"], "required": ["company", "quarter", "gmv(usdbn)", "revenue(usdbn)", "netprofit(usdbn)"], "eval_pipeline": {"gmv(usdbn)": {"preprocess": ["extract_number"], "metric": ["number_near"], "criterion": 0.05}, "revenue(usdbn)": {"preprocess": ["extract_number"], "metric": ["number_near"], "criterion": 0.05}, "netprofit(usdbn)": {"preprocess": ["extract_number"], "metric": ["number_near"], "criterion": 0.05}, "company": {"preprocess": ["norm_str"], "metric": ["exact_match"]}, "quarter": {"preprocess": ["norm_str"], "metric": ["exact_match"]}}}


Question:
We are going to visit America this summer and we love natural scenery, especially hills and mountains. Offer me a list of the U.S. National Parks, including the park's name, located in which states, the price of Standard Entrance Pass for one adult without a vehicle and whether it has hills or mountains. Considering the price may change, just give me the price in June, 2025.
Formatting Notes:
States: If the national park spans more than one state, name them all and separate them with a comma.
Price (per adult): All amounts should be in US dollars and you should only fill in number, just like 5. If the park is free of charge, fill in Free.
Mountain Scenery: Fill in Yes if there are hills or mountains, otherwise fill in No.

Please organize the results in one Markdown table with the following columns:
Park, States, Price (per adult), Mountain Scenery

Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

{"unique_columns": ["park"], "required": ["park", "states", "price(peradult)", "mountainscenery"], "eval_pipeline": {"price(peradult)": {"preprocess": ["norm_str"], "metric": ["exact_match"]}, "mountainscenery": {"preprocess": ["norm_str"], "metric": ["exact_match"]}, "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."}, "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."}}}


Question:
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?

Present the data in one Markdown table with columns: Museum, Address, Business Hours, Admission Fee Status (free or charged).

Note: Address should be in the form No. road/street, District, Seoul. For example, 37 Samcheong-ro, Jongno District, Seoul.

Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

{"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."}}}


Question:
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."

Please output the organized data in the format of one Markdown table.
The column names are as follows in sequence: Ranking, Library, Institution, Library ID, Total Volumes, Funding Source.
Don't ask me any questions, just output the results according to the column without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```

{"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."}}}


Question:
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.

Please organize the results in one Markdown table with the following columns:
Year
Event
Event Venue
Roster of Female Competitors
Final Position

Use '-' if no relevant information is available.

Don't ask me any questions, just output the results according to the columns without omitting cells arbitrarily. The output format is ```markdown
{data_content}
```.

{"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."}}}