context stringlengths 362 4.81k | question stringlengths 2.11k 2.25k | answer stringlengths 1 260 | max_new_tokens int64 128 128 | answer_prefix stringclasses 1
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Table InputTable: [["Place", "Player", "Country", "Score", "To par"], ["1", "Retief Goosen", "South Africa", "68-70-69=207", "–3"], ["T7", "K. J. Choi", "South Korea", "69-70-74=213", "+3"], ["T7", "Lee Westwood", "England", "68-72-73=213", "+3"], ["T7", "Tiger Woods", "United States", "70-71-72=213", "+3"], ["T7", "Peter Hedblom", "Sweden", "77-66-70=213", "+3"], ["T4", "Mark Hensby", "Australia", "71-68-72=211", "+1"], ["T4", "Michael Campbell", "New Zealand", "71-69-71=211", "+1"], ["T2", "Olin Browne", "United States", "67-71-72=210", "E"], ["T2", "Jason Gore", "United States", "71-67-72=210", "E"], ["6", "David Toms", "United States", "70-72-70=212", "+2"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:is retief goosen above or below k.j. choi in ranking? | Above | 128 | Answer: |
Table InputTable: [["District", "Location", "Communities served"], ["Hawken School", "Gates Mills, Ohio", "College preparatory day school: online application, site visit and testing"], ["Solon/Bainbridge Montessori School of Languages", "Bainbridge Township, Ohio", "nonsectarian Montessori School: quarterly enrollment periods"], ["Hershey Montessori Farm School", "Huntsburg Township, Ohio", "parent-owned, and chartered by Ohio Department of Education: application deadline January each year"], ["Notre Dame-Cathedral Latin", "Munson Township, Ohio", "Roman Catholic Diocese of Cleveland: open to 8th grade students who have attended a Catholic elementary school and others who have not"], ["Saint Anselm School", "Chester Township, Ohio", "Roman Catholic Diocese of Cleveland K - 8th grade; preschool"], ["Agape Christian Academy", "Burton Township, Ohio and Troy Township, Ohio", "Accepts applications prior to the start of each school year"], ["Saint Helen's School", "Newbury, Ohio", "Roman Catholic Diocese of Cleveland K - 8th grade; parishioners and non-parishioners"], ["Saint Mary's School", "Chardon, Ohio", "Roman Catholic Diocese of Cleveland preschool - 8th grade; parishioners and non-parishioners"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what is the only school with two locations? | Agape Christian Academy | 128 | Answer: |
Table InputTable: [["Year", "Competition", "Venue", "Position", "Event", "Notes"], ["1996", "Olympic Games", "Atlanta, United States", "20th", "Marathon", "2:17:27"], ["1992", "Olympic Games", "Barcelona, Spain", "5th", "Marathon", "2:14:15"], ["1987", "World Championships", "Rome, Italy", "13th", "Marathon", "2:17:45"], ["1991", "World Championships", "Tokyo, Japan", "6th", "Marathon", "2:15:58"], ["1987", "Venice Marathon", "Venice, Italy", "1st", "Marathon", "2:10:01"], ["1993", "World Championships", "Stuttgart, Germany", "—", "Marathon", "DNF"], ["1986", "Venice Marathon", "Venice, Italy", "1st", "Marathon", "2:18:44"], ["1990", "European Championships", "Split, FR Yugoslavia", "4th", "Marathon", "2:17:45"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:which venue is listed the most? | Venice, Italy | 128 | Answer: |
Table InputTable: [["Year", "Division", "League", "Regular Season", "Playoffs", "Open Cup"], ["2007", "4", "USL PDL", "3rd, Heartland", "Did not qualify", "1st Round"], ["2012", "4", "USL PDL", "5th, Heartland", "Did not qualify", "Did not qualify"], ["2009", "4", "USL PDL", "6th, Heartland", "Did not qualify", "Did not qualify"], ["2008", "4", "USL PDL", "5th, Heartland", "Did not qualify", "Did not qualify"], ["2013", "4", "USL PDL", "4th, Heartland", "Did not qualify", "Did not qualify"], ["1999", "4", "USL PDL", "5th, Heartland", "Did not qualify", "Did not qualify"], ["2011", "4", "USL PDL", "4th, Heartland", "Did not qualify", "Did not qualify"], ["2000", "4", "USL PDL", "4th, Rocky Mountain", "Did not qualify", "Did not qualify"], ["2005", "4", "USL PDL", "3rd, Heartland", "Did not qualify", "Did not qualify"], ["2004", "4", "USL PDL", "6th, Heartland", "Did not qualify", "Did not qualify"], ["2003", "4", "USL PDL", "5th, Heartland", "Did not qualify", "Did not qualify"], ["2010", "4", "USL PDL", "7th, Heartland", "Did not qualify", "Did not qualify"], ["2002", "4", "USL PDL", "5th, Heartland", "Did not qualify", "Did not qualify"], ["2006", "4", "USL PDL", "3rd, Heartland", "Did not qualify", "Did not qualify"], ["2001", "4", "USL PDL", "5th, Rocky Mountain", "Did not qualify", "Did not qualify"], ["1998", "4", "USISL PDSL", "4th, Central", "Division Finals", "1st Round"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what is the total number of open cup's the team qualified for? | 2 | 128 | Answer: |
Table InputTable: [["Town name", "County", "Established", "Disestablished", "Current Status", "Remarks"], ["Padonia", "Brown County", "1850s", "Post office closed in 1933", "Padonia lies among a cluster of houses and farm fields.", "Padonia was the site of a bloodless skirmish in the 1850s called the Battle of Padonia."], ["Veteran", "Stanton County", "1885", "1886", "The exact location of the first townsite of Veteran is unknown but the second location became Johnson City in 1886.", "The town of Veteran was apparently founded by Civil War Veterans."], ["Votaw", "Montgomery County", "1881", "1900", "Nothing remains of the townsite.", "Votaw was an experimental colony founded by African-Americans. After 1900, the population slowly moved elsewhere. The last building burned down in 1915."], ["Thurman", "Chase County", "1874", "1944", "Little remains of the townsite.", ""], ["Achilles", "Rawlins County", "1875", "Post office closed in 1951.", "Only a cemetery remains.", "Achilles was the site of the Battle of Sappa Creek in 1875, it was one of the bloodiest Indian battles fought in northwest Kansas."], ["Woodsdale", "Stevens County", "1885", "late 1880s", "Nothing remains of the townsite.", "Battled with Hugoton for county seat of Stevens County."], ["Mina", "Marshall County", "1889", "1940s", "A railroad town, founded in 1889. The property fell into private hands and was plowed under.", ""], ["Octagon City", "Allen County", "1855", "1856", "Nothing remains of the townsite", "Octagon City was a social experiment where the settlers of the town vowed to eat no meat. The town was so called because the main streets were laid out in an octagon."], ["Hickory Point", "Jefferson County", "1855", "", "A Kansas State Historical Marker is near the location along U.S. 59.", "Location of the Battle of Hickory Point, a skirmish between pro-slavery and free state forces."], ["Tontzville", "Miami County", "1866", "1874", "Nothing remains of the townsite.", ""], ["Cain City", "Rice County", "1881", "1889", "After the founder, Roger Cain, died, the town was slowly abandoned. Nothing remains of the townsite.", ""], ["Boyd", "Barton County", "1886", "1930s", "Some abandoned buildings and ruins remain.", ""], ["Burntwood City", "Rawlins County", "1860s", "", "Nothing remains of the townsite.", ""], ["Old Kiowa", "Barber County", "1872", "1884", "Nothing remains of the townsite.", "Old Kiowa was abandoned when the railroad was built four miles to the south and a new Kiowa was established."], ["Fort Cavagnial", "Leavenworth County", "1744", "1764", "Nothing remains of the old fort.", "Cavagnial is an old French fort and trading post. When Lewis and Clark came through the area in 1804, they saw no sign of the old fort. The exact location is unknown."], ["Ulysses", "Grant County", "1885", "", "The old Ulysses townsite is currently on private property but the \"new\" Ulysses site has an estimated population of 5,557 as of 2008.", "In 1908, Ulysses moved three miles down the road to a new location in an attempt to not pay back bonds that had become due."], ["Terra Cotta", "Ellsworth County", "1867", "1888", "Nothing remains of the townsite.", "Ironically, in 1901, a railroad built a depot at Terra Cotta despite nothing being there. It was moved in 1934."], ["Wherry", "Rice County", "", "", "Nothing remains of the townsite.", "The last building marking the site burned down in 1967."], ["Bayneville", "Sedgwick County", "1884", "", "Some houses and ruins remain in the area.", ""], ["Zarah", "Barton County", "", "1872", "Nothing remains of the townsite.", "Was originally a part of Fort Zarah which was abandoned in 1869. The last resident left Zarah in 1875."], ["Shaffer", "Rush County", "1892", "", "Little remains of the townsite.", ""]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many ghost towns were, or were near the sites of battles? | 2 | 128 | Answer: |
Table InputTable: [["Season", "Class", "Moto", "Races", "Win", "Podiums", "Pole", "Pts", "Position"], ["2008", "125cc", "Aprilia", "17", "1", "5", "0", "176", "5th"], ["2004", "125cc", "Aprilia", "1", "0", "0", "0", "0", "NC"], ["2009", "125cc", "Aprilia", "16", "1", "4", "0", "179.5", "3rd"], ["2011", "125cc", "Aprilia", "16", "8", "11", "7", "302", "1st"], ["2005", "125cc", "Derbi", "13", "0", "0", "0", "1", "36th"], ["2010", "125cc", "Aprilia", "16", "3", "14", "1", "296", "2nd"], ["2007", "125cc", "Derbi", "17", "0", "0", "0", "19", "22nd"], ["2006", "125cc", "Derbi", "16", "0", "0", "0", "53", "14th"], ["2014", "Moto2", "Suter", "1", "0", "0", "0", "0*", "NC*"], ["2012", "Moto2", "Suter", "17", "0", "1", "0", "37", "17th"], ["Total", "", "", "147", "16", "39", "9", "1213.5", ""], ["2013", "Moto2", "Suter", "17", "3", "4", "1", "150", "7th"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:is the term derbi or aprilia listed more? | Aprilia | 128 | Answer: |
Table InputTable: [["Year", "Competition", "Venue", "Position", "Event", "Notes"], ["2000", "World Junior Championships", "Santiago, Chile", "1st", "400 m hurdles", "49.23"], ["2003", "European U23 Championships", "Bydgoszcz, Poland", "1st", "400 m hurdles", "48.45"], ["2004", "Olympic Games", "Athens, Greece", "6th", "400 m hurdles", "49.00"], ["2007", "World Championships", "Osaka, Japan", "3rd", "400 m hurdles", "48.12 (NR)"], ["2001", "World Championships", "Edmonton, Canada", "18th (sf)", "400 m hurdles", "49.80"], ["2003", "European U23 Championships", "Bydgoszcz, Poland", "1st", "4x400 m relay", "3:03.32"], ["2002", "European Championships", "Munich, Germany", "4th", "400 m", "45.40"], ["2007", "World Championships", "Osaka, Japan", "3rd", "4x400 m relay", "3:00.05"], ["2006", "European Championships", "Gothenburg, Sweden", "2nd", "400 m hurdles", "48.71"], ["2002", "European Indoor Championships", "Vienna, Austria", "1st", "400 m", "45.39 (CR, NR)"], ["2001", "Universiade", "Beijing, China", "8th", "400 m hurdles", "49.68"], ["2008", "Olympic Games", "Beijing, China", "6th", "400 m hurdles", "48.42"], ["2012", "European Championships", "Helsinki, Finland", "18th (sf)", "400 m hurdles", "50.77"], ["2004", "Olympic Games", "Athens, Greece", "10th (h)", "4x400 m relay", "3:03.69"], ["1999", "European Junior Championships", "Riga, Latvia", "4th", "400 m hurdles", "52.17"], ["2003", "World Indoor Championships", "Birmingham, United Kingdom", "3rd", "4x400 m relay", "3:06.61"], ["2002", "European Indoor Championships", "Vienna, Austria", "1st", "4x400 m relay", "3:05.50 (CR)"], ["2003", "World Indoor Championships", "Birmingham, United Kingdom", "7th (sf)", "400 m", "46.82"], ["2002", "European Championships", "Munich, Germany", "8th", "4x400 m relay", "DQ"], ["2008", "Olympic Games", "Beijing, China", "7th", "4x400 m relay", "3:00.32"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many times did plawgo come in first place? | 5 | 128 | Answer: |
Table InputTable: [["Year", "Recipient", "Nationality", "Profession", "Speech"], ["1998", "Herman Wouk", "United States", "Professional writer and 1952 Pulitzer Prize winner", ""], ["1997", "Elie Wiesel", "United States", "Professional writer\\nWinner of the Nobel Peace Prize (1986)", ""], ["2005", "William Safire", "United States", "Author, journalist and speechwriter\\n1978 Pulitzer Prize winner", ""], ["2007", "Norman Podhoretz", "United States", "Author, columnist", ""], ["2003", "Ruth Roskies Wisse", "United States", "Yiddish professor of Harvard University", "[2]"], ["1999", "A.M. Rosenthal", "United States", "Former New York Times editor\\nFormer New York Daily News columnist", ""], ["2006", "Daniel Pipes", "United States", "Author and historian", ""], ["2008", "David Be'eri, Mordechai Eliav, Rabbi Yehuda Maly", "Israel", "", ""], ["2002", "Charles Krauthammer", "United States", "The Washington Post columnist", "[1]"], ["2001", "Cynthia Ozick", "United States", "Professional writer", ""], ["2009", "Caroline Glick", "Israel", "Journalist", ""], ["2010", "Malcolm Hoenlein", "United States", "Executive Vice Chairman of the Conference of Presidents of Major American Jewish Organizations", ""], ["2000", "Sir Martin Gilbert", "United Kingdom", "Historian and writer", ""], ["2004", "Arthur Cohn", "Switzerland", "Filmmaker and writer", ""]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many years before his 1998 guardian of zion award did herman wouk win the pulitzer prize? | 46 | 128 | Answer: |
Table InputTable: [["Series", "Years", "Volumes", "Writer", "Editor", "Remarks"], ["Ivan Zourine", "1979", "2", "Jacques Stoquart", "Magic-Strip", ""], ["Edmund Bell", "1987–1990", "4", "Jacques Stoquart and Martin Lodewijk", "Cl. Lefrancq", "Based on the stories by John Flanders (Jean Ray)"], ["Shelena", "2005", "1", "Jéromine Pasteur", "Casterman", ""], ["Alain Brisant", "1985", "1", "Maurice Tillieux", "Dupuis", ""], ["L'affaire Dominici", "2010", "1", "Pascal Bresson", "Glénat", ""], ["Valhardi", "1984–1986", "2", "Jacques Stoquart and André-Paul Duchâteau", "Dupuis", "Continuation of the series after Jijé and Eddy Paape"], ["Terreur", "2002–2004", "2", "André-Paul Duchâteau", "Le Lombard", "Fictional biography of Madame Tussaud"], ["Harricana", "1992", "1", "Jean-Claude de la Royère", "Claude Lefrancq", "Drawn by Denis Mérezette, Follet did the page lay-out"], ["Marshall Blueberry", "1994", "1", "Jean Giraud", "Alpen", "Drawn by William Vance, Follet did the page lay-out"], ["Ikar", "1995–1997", "2", "Pierre Makyo", "Glénat", ""], ["L'Iliade", "1982", "1", "Jacques Stoquart", "Glénat", "Adapted from the Ilias by Homer"], ["Bruno Brazil", "1973–1977", "5", "Greg", "Magic-Strip", "William Vance drew the comics, Follet provided the page lay-out"], ["Jacques Le Gall", "1984–1985", "2", "Jean-Michel Charlier", "Dupuis", "A collaboration with MiTacq"], ["Daddy", "1991-92", "2", "Loup Durand", "Cl. Lefrancq", ""], ["Bob Morane", "1991–2000", "3", "Henri Vernes", "Nautilus and Claude Lefrancq", "Follet drew one story in 2000, and made the cover art for two others (drawn by Gerald Forton)"], ["Les zingari", "2004–2005", "2", "Yvan Delporte", "Hibou", ""], ["Les autos de l'aventure", "1996–1998", "2", "De la Royère", "Citroën", "Promotional comics"], ["L'étoile du soldat", "2007", "1", "Christophe De Ponfilly", "Casterman", "Announced (28 August 2007)"], ["Steve Severin", "1981–2003", "9", "Jacques Stoquart and Yvan Delporte", "Glénat", "3 in French - 6 additional in Dutch"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what is the difference in volumes between ivan zourine and edmund bell? | 2 | 128 | Answer: |
Table InputTable: [["Year", "Winners", "Runners-up", "Third", "Fourth"], ["1989", "Real Zaragoza", "Club de Fútbol Atlante", "Aragon", "-"], ["1992", "Real Zaragoza", "Fútbol Club Barcelona", "-", "-"], ["1998", "Parma", "Real Zaragoza", "-", "-"], ["2004", "Club Atlético de Madrid", "Real Zaragoza", "-", "-"], ["1985", "Fútbol Club Barcelona", "Real Zaragoza", "-", "-"], ["1987", "Real Zaragoza", "Checoslovaquia", "-", "-"], ["1988", "Club Atlético Peñarol", "Real Zaragoza", "-", "-"], ["1971", "Cologne", "Royal Sporting Club Anderlecht", "Real Zaragoza", "-"], ["2001", "Real Zaragoza", "FC Twente", "-", "-"], ["2000", "Real Zaragoza", "Parma", "-", "-"], ["2007", "Real Zaragoza", "Juventus Football Club", "-", "-"], ["1995", "Real Zaragoza", "Club Nacional de Football", "-", "-"], ["2005", "Real Zaragoza", "Real Madrid Club de Fútbol", "-", "-"], ["1991", "Real Zaragoza", "Dinamo Bucharest", "-", "-"], ["1993", "Club de Regatas Vasco da Gama", "Real Zaragoza", "-", "-"], ["1984", "Videoton SC", "Universidad Católica", "Real Zaragoza", "Defensor Sporting Club"], ["1996", "Real Zaragoza", "Hamburg SV", "-", "-"], ["2002", "Real Zaragoza", "Athletic Club", "-", "-"], ["1999", "Real Zaragoza", "Feyenoord Rotterdam", "-", "-"], ["1972", "Hamburg SV", "Sociedade Esportiva Palmeiras", "Real Zaragoza", "-"], ["1986", "Real Zaragoza", "Cologne", "-", "-"], ["2008", "Getafe Club de Fútbol", "Real Zaragoza", "-", "-"], ["2009", "Società Sportiva Lazio", "Real Zaragoza", "-", "-"], ["1997", "Società Sportiva Lazio", "Real Zaragoza", "-", "-"], ["1983", "Real Zaragoza", "Club América", "Aston Villa Football Club", "Politehnica Timişoara"], ["1980", "RCD Espanyol", "Real Zaragoza", "Sporting Lisboa", "Partizan Belgrade"], ["1974", "Real Zaragoza", "Eintracht Frankfurt", "FC Molenbeek Brussels Strombeek", "Partizan Belgrade"], ["1976", "Real Zaragoza", "Górnik Zabrze", "OFK Belgrade", "Olympiacos FC"], ["2010", "Sociedad Deportiva Huesca", "Real Zaragoza", "CD Teruel", "-"], ["2012", "Real Zaragoza", "RCD Espanyol", "-", "-"], ["2011", "Real Zaragoza", "RCD Espanyol", "-", "-"], ["1981", "Real Zaragoza", "Nottingham Forest Football Club", "Tisza Volán SC", "Club Atlético Osasuna"], ["2006", "Real Zaragoza", "Associazione Sportiva Livorno Calcio", "-", "-"], ["1990", "FC Dinamo Moscow", "Real Zaragoza", "Real Betis Balompié", "-"], ["2003", "Real Zaragoza", "Chievo", "-", "-"], ["1975", "Real Zaragoza", "Club Atlético Boca Juniors", "FK Vojvodina", "Boavista Futebol Clube"], ["1977", "PFC CSKA Sofia", "Real Zaragoza", "FK Radnički Niš", "RCD Espanyol"], ["1973", "Borussia Mönchengladbach", "PFC CSKA Sofia", "Real Zaragoza", "West Ham"], ["1979", "Real Zaragoza", "NK Dinamo Zagreb", "Vasas SC", "FK Sarajevo"], ["1982", "Manchester United F.C.", "Real Zaragoza", "MTK Hungária FC", "Budapest Honvéd FC"], ["1994", "Real Zaragoza", "CSKA Moscow", "-", "-"], ["1978", "Real Zaragoza", "Club Nacional de Football", "PFC Sliven", "FK Trepca Mitrovica"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:who was the first winner from spain? | Real Zaragoza | 128 | Answer: |
Table InputTable: [["Outcome", "No.", "Date", "Tournament", "Surface", "Partner", "Opponents", "Score"], ["Winner", "6.", "28 October 2013", "Barnstaple, United Kingdom", "Hard (i)", "Naomi Broady", "Raluca Olaru\\n Tamira Paszek", "6–3, 3–6, [10–5]"], ["Winner", "3.", "23 January 2012", "Andrézieux-Bouthéon, France", "Hard (i)", "Karolína Plíšková", "Julie Coin\\n Eva Hrdinová", "6–4, 4–6, [10–5]"], ["Winner", "4.", "30 January 2012", "Grenoble, France", "Hard (i)", "Karolína Plíšková", "Valentyna Ivakhnenko\\n Maryna Zanevska", "6–1, 6–3"], ["Winner", "5.", "12 November 2012", "Zawada, Poland", "Carpet (i)", "Karolína Plíšková", "Kristina Barrois\\n Sandra Klemenschits", "6–3, 6–1"], ["Runner-up", "4.", "17 September 2012", "Shrewsbury, United Kingdom", "Hard (i)", "Karolína Plíšková", "Vesna Dolonc\\n Stefanie Vögele", "1–6, 7–6(7–3), [13–15]"], ["Runner-up", "2.", "6 November 2011", "Taipei 5, Taiwan", "Hard", "Karolína Plíšková", "Chan Yung-jan\\n Zheng Jie", "6–7(5–7), 7–5, 3–6"], ["Winner", "1.", "13 February 2011", "Rancho Mirage, United States", "Hard", "Karolína Plíšková", "Nadejda Guskova\\n Sandra Zaniewska", "6–7(6–8), 6–1, 6–4"], ["Winner", "2.", "7 August 2011", "Vancouver, Canada", "Hard", "Karolína Plíšková", "Jamie Hampton\\n N. Lertcheewakarn", "5–7, 6–2, 6–4"], ["Runner-up", "1.", "16 May 2010", "Kurume, Japan", "Clay", "Karolína Plíšková", "Sun Shengnan\\n Xu Yifan", "0–6, 3–6"], ["Runner-up", "3.", "20 November 2011", "Bratislava, Slovakia", "Hard", "Karolína Plíšková", "Naomi Broady\\n Kristina Mladenovic", "7–5, 4–6, [2–10]"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:are the dates set in a consecutive order? | yes | 128 | Answer: |
Table InputTable: [["Year", "Winner", "Jockey", "Trainer", "Owner", "Distance\\n(Miles)", "Time", "Win $"], ["1992", "November Snow", "Chris Antley", "H. Allen Jerkens", "Earle I. Mack", "1", "1:35.91", "$66,480"], ["2003", "Indy Five Hundred", "Pat Day", "Robert Barbara", "Georgica Stable", "1-1/8", "1:48.44", "$150,000"], ["1979", "Danielle B.", "Ruben Hernandez", "John O. Hertler", "Our Precious Stable", "1-1/16", "1:45.40", "$33,000"], ["1978", "Late Bloomer", "Jorge Velasquez", "John M. Gaver, Jr.", "Greentree Stable", "1-1/16", "1:41.60", ""], ["1989", "Highest Glory", "Jose A. Santos", "D. Wayne Lukas", "H. Joseph Allen", "1", "1:37.20", "$70,440"], ["1995", "Perfect Arc", "John Velazquez", "Angel Penna, Jr.", "Brazil Stables", "1-1/16", "1:42.35", "$101,070"], ["2012", "Samitar", "Ramon Dominguez", "Chad C. Brown", "Martin S. Schwartz", "1-1/8", "1:48.74", "$180,000"], ["1983", "Pretty Sensible", "Alfredo Smith, Jr.", "George Travers", "John Zervas", "1", "1:37.80", "$33,600"], ["1980", "Mitey Lively", "Jorge Velasquez", "Douglas R. Peterson", "Tayhill Stable", "1", "1:36.40", "$33,480"], ["1999", "Perfect Sting", "Pat Day", "Joseph Orseno", "Stronach Stable", "1-1/8", "1:49.41", "$129,900"], ["1984", "Given", "Matthew Vigliotti", "Alfino Pepino", "Ronald S. Green", "1-1/16", "1:43.40", "$42,960"], ["2011", "Winter Memories", "Javier Castellano", "James J. Toner", "Phillips Racing Partnership", "1-1/8", "1:51.06", "$150,000"], ["1993", "Sky Beauty", "Mike E. Smith", "H. Allen Jerkens", "Georgia E. Hofmann", "1", "1:35.76", "$68,400"], ["1988", "Topicount", "Angel Cordero, Jr.", "H. Allen Jerkens", "Centennial Farms", "1", "1:38.00", "$82,260"], ["1991", "Dazzle Me Jolie", "Jose A. Santos", "Willard J. Thompson", "Jolie Stanzione", "1", "1:35.61", "$72,000"], ["2009", "Miss World", "Cornelio Velasquez", "Christophe Clement", "Waratah Thoroughbreds", "1-1/8", "1:53.55", "$180,000"], ["2010", "Check the Label", "Ramon Dominguez", "H. Graham Motion", "Lael Stables", "1-1/8", "1:51.41", "$150,000"], ["2007", "Alexander Tango", "Shaun Bridgmohan", "Tommy Stack", "Noel O' Callaghan", "1-1/8", "1:48.97", "$120,000"], ["2000", "Gaviola", "Jerry D. Bailey", "William H. Turner, Jr.", "Twilite Farms", "1-1/8", "1:48.89", "$150,000"], ["2001", "Voodoo Dancer", "Corey Nakatani", "Christophe Clement", "Green Hills Farms", "1-1/8", "1:47.69", "$150,000"], ["1986", "Life At The Top", "Chris McCarron", "D. Wayne Lukas", "Lloyd R. French", "1", "1:34.40", "$51,210"], ["2002", "Wonder Again", "Edgar Prado", "James J. Toner", "Joan G. & John W. Phillips", "1-1/8", "1:47.33", "$150,000"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:in what year was the least money won? | 1979 | 128 | Answer: |
Table InputTable: [["Season", "Club", "Competition", "Games", "Goals"], ["2005/06", "KSV Roeselare", "Jupiler League", "26", "0"], ["2007/08", "KSV Roeselare", "Jupiler League", "25", "0"], ["2002/03", "RAEC Mons", "Jupiler League", "19", "0"], ["2006/07", "KSV Roeselare", "Jupiler League", "29", "1"], ["2010/11", "Kortrijk", "Jupiler League", "0", "0"], ["2004/05", "KSV Roeselare", "Belgian Second Division", "29", "1"], ["2003/04", "RAEC Mons", "Jupiler League", "23", "0"], ["2008/09", "Excelsior Mouscron", "Jupiler League", "31", "1"], ["2009/10", "Győri ETO FC", "Soproni Liga", "1", "0"], ["2009/10", "Excelsior Mouscron", "Jupiler League", "14", "1"], ["", "", "Totaal", "278", "4"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what was the average number of goals scored? | 1 | 128 | Answer: |
Table InputTable: [["Region", "Physician (GP & specialist)", "Physician : Population Ratio", "Health Officer", "HO : Population Ratio", "All Nurses", "Nurse : Population Ratio", "Mid-wives", "Mid Wife: Population Ratio", "HEW*", "HEW : Population Ratio"], ["SNNPR", "242", "1:65,817", "220", "1:72,398", "3,980", "1:4,002", "316", "1:50,404", "7,915", "1:2,012"], ["Amhara", "304", "1:58,567", "434", "1:41,024", "3,790", "1:4,698", "212", "1:83,983", "7,471", "1:2,383"], ["Addis Ababa", "934", "1:3,056", "170", "1:16,791", "3,377", "1:845", "244", "1:11,699", "NA", "-"], ["Somalia", "71", "1:65,817", "12", "1:389,415", "314", "1:14,882", "45", "1:103,844", "1,427", "1:3,275"], ["Ben-Gumuz", "12", "1:59,309", "42", "1:16,945", "452", "1:1,575", "37", "1:19,235", "499", "1:1,426"], ["Diredawa", "53", "1:6,796", "19", "1:18,957", "272", "1:1,324", "20", "1:18,009", "142", "1:2,537"], ["Tigray", "101", "1:44,880", "188", "1:24,111", "2,332", "1:1,944", "185", "1:24,502", "1,433", "1:3,163"], ["Gambella", "13", "1:25,585", "13", "1:25,585", "91", "1:3,655", "4", "1:83,150", "457", "1:728"], ["Oromia", "378", "1:76,075", "448", "1:64,189", "5,040", "1:5,706", "287", "1:100,197", "13856", "1:2,075"], ["Harari", "29", "1:6,655", "31", "1:6,226", "276", "1:699", "29", "1:6,655", "47", "1:4,106"], ["Afar", "15", "1:98,258", "29", "1:50,823", "185", "1:7,967", "−", "−", "572", "1:2,577"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:which region has the highest number of nurses? | Oromia | 128 | Answer: |
Table InputTable: [["Season", "Episodes", "Season Premiere", "Season Finale"], ["5", "40", "October 12, 2009", "June 14, 2010"], ["4", "48", "October 13, 2008", "May 11, 2009"], ["3", "44", "October 15, 2007", "June 2, 2008"], ["2", "52", "October 7, 2006", "July 16, 2007"], ["6", "20", "September 6, 2010", "December 6, 2010"], ["1", "20", "March 4, 2006", "May 13, 2006"], ["7", "8", "October 29, 2013", "December 17, 2013"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:did season 5 start in october or september? | October | 128 | Answer: |
Table InputTable: [["Week", "Date", "TV Time", "Opponent", "Result", "Attendance"], ["4", "September 27, 1998", "FOX 10:00 am MT", "at St. Louis Rams", "W 20–17", "55,832"], ["9", "November 1, 1998", "FOX 11:00 am MT", "at Detroit Lions", "W 17–15", "66,087"], ["3", "September 20, 1998", "ESPN 5:15 pm MT", "Philadelphia Eagles", "W 17–3", "39,782"], ["6", "October 11, 1998", "FOX 1:05 pm MT", "Chicago Bears", "W 20–7", "50,495"], ["15", "December 13, 1998", "FOX 11:00 am MT", "at Philadelphia Eagles", "W 20–17", "62,176"], ["14", "December 6, 1998", "FOX 2:15 pm MT", "New York Giants", "L 23–19", "46,128"], ["13", "November 29, 1998", "FOX 11:00 am MT", "at Kansas City Chiefs", "L 34–24", "69,613"], ["16", "December 20, 1998", "FOX 2:05 pm MT", "New Orleans Saints", "W 19–17", "51,617"], ["12", "November 22, 1998", "FOX 11:00 am MT", "at Washington Redskins", "W 45–42", "63,435"], ["17", "December 27, 1998", "CBS 2:05 pm MT", "San Diego Chargers", "W 16–13", "71,670"], ["7", "October 18, 1998", "FOX 10:00 am MT", "at New York Giants", "L 34–7", "70,456"], ["10", "November 8, 1998", "FOX 2:15 pm MT", "Washington Redskins", "W 29–27", "45,950"], ["5", "October 4, 1998", "CBS 1:05 pm MT", "Oakland Raiders", "L 23–20", "53,240"], ["2", "September 13, 1998", "FOX 1:15 pm MT", "at Seattle Seahawks", "L 33–14", "57,678"], ["1", "September 6, 1998", "FOX 1:05 pm MT", "at Dallas Cowboys", "L 38–10", "63,602"], ["11", "November 15, 1998", "FOX 2:15 pm MT", "Dallas Cowboys", "L 35–28", "71,670"], ["8", "Bye", "Bye", "Bye", "Bye", ""]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what are the number of wins the cardinals earned by the end of the 1998 regular season? | 9 | 128 | Answer: |
Table InputTable: [["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"], ["3", "Finland", "3", "3", "1", "7"], ["1", "Soviet Union", "*7*", "3", "6", "16"], ["7", "Norway", "2", "1", "1", "4"], ["4", "Switzerland", "3", "2", "1", "6"], ["5", "Sweden", "2", "4", "4", "10"], ["8", "Italy", "1", "2", "0", "3"], ["2", "Austria", "4", "3", "4", "11"], ["9", "Germany", "1", "0", "1", "2"], ["10", "Canada", "0", "1", "2", "3"], ["6", "United States", "2", "3", "2", "7"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:finland and what other nation earned three gold medals? | Switzerland | 128 | Answer: |
Table InputTable: [["#", "Directed By", "Written By", "Original Air Date"], ["7", "Frank W. Smith", "Fran Carroll", "November 9, 1997"], ["9", "Douglas Mackinnon", "Neil McKay", "November 23, 1997"], ["8", "Douglas Mackinnon", "Neil McKay", "November 16, 1997"], ["14", "Ken Horn", "Neil McKay", "January 25, 1998"], ["10", "John Reardon", "Simon J. Sharkey", "November 30, 1997"], ["12", "Ken Horn", "David Humphries", "January 11, 1998"], ["6", "John Reardon", "Neil McKay", "November 2, 1997"], ["16", "Douglas MacKinnon", "Neil McKay", "February 8, 1998"], ["4", "Gerry Poulson", "David Humphries", "October 12, 1997"], ["5", "John Reardon", "Neil McKay", "October 26, 1997"], ["3", "Gerry Poulson", "David Humphries", "October 5, 1997"], ["15", "Frank W. Smith", "Dave Humphries", "February 1, 1998"], ["11", "John Reardon", "Simon J. Sharkey", "January 4, 1998"], ["17", "Graham Moore", "Simon J. Sharkey", "February 15, 1998"], ["18", "John Reardon", "Simon J. Sharkey", "February 22, 1998"], ["2", "Frank W. Smith", "Simon J. Sharkey", "September 28, 1997"], ["13", "John Reardon", "Simon J. Sharkey", "January 18, 1998"], ["1", "Frank W. Smith", "Simon J. Sharkey", "September 14, 1997"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many episodes in series 10 did fran carroll write? | 1 | 128 | Answer: |
Table InputTable: [["Year", "Organization", "Award", "Work", "Result"], ["2007", "16th Hashida Awards", "Newcomer Award", "Hana Yori Dango 2", "Won"], ["2011", "26th Nikkan Sport Film Awards", "Best Newcomer", "Youkame no Semi, Miracle in the Pacific", "Won"], ["2012", "16th Nikkan Sport Grand Prix", "Best Actress", "Tokkan", "Nominated"], ["2012", "35th Japan Academy Awards", "Best Starring Actress", "Youkame no Semi", "Won"], ["2010", "Nikkan Sports Grand Prix (Fall)", "Best Supporting Actress", "Veterinarian Dolittle", "Nominated"], ["2012", "Japan Film Festival Theater Staff", "Best Actress", "Youkame no Semi", "Won"], ["2011", "TV Navi", "Best Actress", "Ohisama", "Won"], ["2011", "35th Fumiko Yamaji Award Film Awards", "Newcomer Actress", "Youkame no Semi", "Won"], ["2011", "3rd TAMA Film Award", "Best Emerging Actress", "Miracle in the Pacific", "Won"], ["2007", "10th Nikkan Sports Drama Grand Prix", "Best Actress", "Hana Yori Dango 2", "Won"], ["2008", "Nickelodeon Kids' Choice Awards", "Best Actress", "Hana Yori Dango 2", "Won"], ["2007", "2007 MTV Student Voice Awards", "Best Actress", "Hana Yori Dango 2", "Won"], ["2011", "70th The Television Drama Academy Awards", "Best Actress", "Ohisama", "Won"], ["2005", "47th The Television Drama Academy Awards", "Best Actress", "Hana Yori Dango", "Won"], ["2007", "54th The Television Academy Drama Awards", "Best Actress", "First Kiss", "Nominated"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what is the total number of times inoue was nominated, but did not win, an award? | 3 | 128 | Answer: |
Table InputTable: [["Route", "Name", "Fare Type", "Terminals", "Terminals", "Major streets", "Notes", "History"], ["32, 36", "Pennsylvania Avenue Line", "Local", "Friendship Heights station", "32 Southern Avenue station\\n36 Naylor Road station", "Wisconsin Avenue NW\\nPennsylvania Avenue SE/NW\\nBranch Avenue SE (36)\\nAlabama Avenue SE (32)", "Some weekday 32 and 36 trips terminate at:\\n\\nFarragut Square\\nFoggy Bottom – GWU station", "36 replaces a portion of the old 35 (see Pennsylvania Avenue Line)"], ["37", "Wisconsin Avenue Metro Extra Line", "Limited Stop", "Friendship Heights station", "Archives station (AM End)\\nFederal Triangle (10th St & Pennsylvania Av NW) (PM Start)", "Wisconsin Avenue NW\\nMassachusetts Avenue NW\\nPennsylvania Avenue NW", "Weekday peak hour service only (AM to Archives, PM to Friendship Heights)\\nLimited Stops Only", "A prior \"incarnation\" of the 37 was once known as the Wisconsin Avenue Express Line, running from Tenleytown-AU station to Archives until the early 1990s"], ["34", "Naylor Rd Line", "Local", "Archives (10th St & Pennsylvania Av NW)", "Naylor Road station", "Pennsylvania Avenue SE\\nIndependence Avenue SE/SW\\nNaylor Road SE", "", "34 operated to Friendship Heights station until replaced by the M5, which operated from Naylor Road station to Eastern Market station in 2007; 34 replaced the M5 in 2008 with the extension to the Archives station, also see Pennsylvania Avenue Line"], ["N2, N3, N4, N6", "Massachusetts Avenue Line", "Local", "Friendship Heights station", "N2, N4, N6 Farragut Square\\nN3 Federal Triangle (Constitution Av & 10th St NW)", "Western Avenue (N3, N4, N6)\\nWisconsin Avenue (N2)\\nMassachusetts Avenue\\nNew Mexico Avenue NW (N2, N6)\\nConnecticut Avenue NW", "N3: weekday peak hour service only (N3: AM to Federal Triangle, PM to Friendship Heights)\\nN2 & N4: Monday-Friday service only.\\nN6 is a combination of the N2 and N4, operates post PM rush hour weekdays and all day on weekends.", "N3 was part of the Massachusetts Av-Federal Triangle Line (along with the former N1) until 1996, when N1 was eliminated & N3 merged with the N2, N4 & N6.\\nN4 used to terminate at Westmoreland Circle until the late 1990s."], ["31", "Wisconsin Avenue Line", "Local", "Friendship Heights station", "Potomac Park (Virginia Av & 21st St NW)", "Wisconsin Avenue NW", "", "31 replaces the Wisconsin Avenue portion of the old 30 (see Pennsylvania Avenue Line)"], ["52, 53, 54", "14th Street Line", "Local", "Takoma station\\n14th Street & Colorado Ave NW", "52, 54 L'Enfant Plaza Metrorail Station (7th & D Streets SW)\\n53 McPherson Square station (Franklin Square Entrance)", "14th Street NW\\nPennsylvania Avenue NW (54)\\nIndependence Avenue SW (52)", "52 and 54: daily\\n53: Monday-Saturday only", "52 & 54 originally terminated at Navy Yard until the mid-1990s, when the 52 was truncated to L'Enfant Plaza station & 54 to Federal Triangle. 54 was later extended to L'Enfant Plaza station.\\nThe 53 was introduced several years after the former route 50 was discontinued, operating at first to Bureau Of Engraving before being shortened to Federal Triangle and now to Franklin Square.\\nAlso see 14th Street Line"], ["E6", "Chevy Chase Line", "Local", "Knollwood (Knollwood Retirement Home)", "Friendship Heights station", "Western Avenue\\nMcKinley Street NW", "", ""], ["E2, E3, E4", "Military Road-Crosstown Line", "Local", "Friendship Heights station", "E2 Fort Totten station\\nE2, E3 Ivy City (New York Av & Fenwick St NE)\\nE4 Riggs Park (Eastern Av & Jamaica St NE)", "Military Road\\nKennedy Street\\nSouth Dakota Avenue (E2, E3)\\n18th Street NE (E2, E3)", "E2 terminates at Fort Totten station when E3 is in operation (weekends only)\\nE3 also serves Riggs Park (it is a combination of the E2 and E4)", ""]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:other than route 31, name one that starts at the friendship heights station. | 32 | 128 | Answer: |
Table InputTable: [["Date", "Opponent", "Score", "Result", "Record"], ["July 29", "Minnesota", "73-58", "Win", "14-8"], ["July 1", "Minnesota", "71-69", "Win", "8-7"], ["July 19", "@ Sacramento", "74-71", "Win", "12-8"], ["July 8", "Indiana", "60-56", "Win", "10-7"], ["May 22", "Seattle", "75-64", "Win", "1-0"], ["July 18", "@ Los Angeles", "79-74", "Win", "11-8"], ["July 15", "@ Seattle", "55-69", "Loss", "10-8"], ["August 7", "@ Indiana", "68-55", "Win", "17-9"], ["August 31 (First Round, Game 2)", "Sacramento", "69-48", "Win", "1-1"], ["May 24", "@ Phoenix", "69-62", "Win", "2-0"], ["August 19", "Seattle", "52-47", "Win", "20-11"], ["July 5", "Washington", "76-54", "Win", "9-7"], ["June 10", "Sacramento", "71-66", "Win", "4-4"], ["September 2 (First Round, Game 3)", "Sacramento", "68-70", "Loss", "1-2"], ["June 17", "@ Minnesota", "77-68", "Win", "5-5"], ["August 29 (First Round, Game 1)", "@ Sacramento", "59-65", "Loss", "0-1"], ["August 5", "Sacramento", "74-47", "Win", "16-9"], ["July 26", "New York", "61-53", "Win", "13-8"], ["June 28", "San Antonio", "64-49", "Win", "7-7"], ["June 21", "Cleveland", "63-62", "Win", "6-6"], ["August 8", "@ Detroit", "66-56", "Win", "18-9"], ["June 20", "@ San Antonio", "69-76", "Loss", "5-6"], ["August 23", "@ Seattle", "64-71", "Loss", "20-13"], ["August 1", "@ San Antonio", "53-63", "Loss", "14-9"], ["June 1", "@ Minnesota", "64-68 (OT)", "Loss", "2-2"], ["August 2", "San Antonio", "64-55", "Win", "15-9"], ["June 6", "@ Charlotte", "58-69", "Loss", "3-3"], ["May 30", "Connecticut", "83-91", "Loss", "2-1"], ["June 14", "@ Phoenix", "61-76", "Loss", "4-5"], ["June 3", "Phoenix", "66-51", "Win", "3-2"], ["August 25", "@ Los Angeles", "64-67", "Loss", "20-14"], ["August 21", "@ Sacramento", "52-64", "Loss", "20-12"], ["June 7", "@ Connecticut", "58-65", "Loss", "3-4"], ["August 10", "Phoenix", "69-46", "Win", "19-9"], ["August 16", "Los Angeles", "63-64", "Loss", "19-10"], ["June 24", "Los Angeles", "62-71", "Loss", "6-7"], ["August 18", "@ New York", "64-67", "Loss", "19-11"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many games did the team score more than 70 points? | 10 | 128 | Answer: |
Table InputTable: [["Rank", "Lane", "Name", "Nationality", "Time", "Notes"], ["7", "8", "Grant Patterson", "Australia", "55.49", ""], ["2", "4", "David Smetanine", "France", "38.97", "Q"], ["1", "5", "Eskender Mustafaiev", "Ukraine", "38.77", "Q"], ["3", "3", "Kyunghyun Kim", "South Korea", "40.37", "Q"], ["4", "6", "Christoffer Lindhe", "Sweden", "41.52", "Q"], ["6", "2", "Ronystony Cordeiro da Silva", "Brazil", "44.22", ""], ["5", "7", "Arnost Petracek", "Czech Republic", "43.12", ""], ["8", "1", "Arnulfo Castorena", "Mexico", "1:03.49", ""]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many swimmers qualified? | 4 | 128 | Answer: |
Table InputTable: [["County", "Brown", "Votes", "Nixon", "Votes", "Wyckoff", "Votes"], ["Santa Barbara", "47.50%", "30,424", "51.24%", "32,821", "1.26%", "807"], ["San Joaquin", "49.40%", "43,276", "49.25%", "43,147", "1.34%", "1,178"], ["Nevada", "51.02%", "4,818", "47.12%", "4,450", "1.85%", "175"], ["Butte", "47.74%", "16,142", "50.79%", "17,172", "1.47%", "497"], ["Siskiyou", "59.98%", "7,718", "38.41%", "4,942", "1.62%", "208"], ["San Luis Obispo", "52.86%", "16,110", "45.36%", "13,825", "1.78%", "543"], ["Riverside", "46.60%", "50,257", "51.86%", "55,926", "1.54%", "1,666"], ["Sonoma", "49.19%", "29,373", "49.65%", "29,647", "1.17%", "696"], ["Lake", "44.42%", "3,315", "54.15%", "4,041", "1.43%", "107"], ["Fresno", "57.78%", "68,187", "40.85%", "48,211", "1.37%", "1,615"], ["San Bernardino", "51.68%", "88,437", "46.78%", "80,054", "1.54%", "2,634"], ["San Diego", "42.40%", "153,389", "55.83%", "201,969", "1.77%", "6,416"], ["Sacramento", "60.69%", "115,462", "37.74%", "71,788", "1.57%", "2,988"], ["Santa Cruz", "44.93%", "17,354", "53.28%", "20,580", "1.79%", "690"], ["Merced", "57.62%", "14,105", "41.14%", "10,071", "1.23%", "302"], ["El Dorado", "56.25%", "6,572", "41.44%", "4,842", "2.30%", "269"], ["Contra Costa", "55.49%", "91,150", "43.34%", "71,192", "1.18%", "1,935"], ["Santa Clara", "51.20%", "121,149", "47.63%", "112,700", "1.18%", "2,783"], ["San Francisco", "62.19%", "180,298", "36.96%", "107,165", "0.85%", "2,455"], ["Mendocino", "51.50%", "8,704", "46.96%", "7,936", "1.54%", "261"], ["Napa", "53.50%", "14,748", "44.72%", "12,326", "1.78%", "490"], ["San Mateo", "51.88%", "90,464", "47.09%", "82,115", "1.03%", "1,797"], ["Alameda", "57.98%", "206,861", "40.88%", "145,851", "1.13%", "4,038"], ["Los Angeles", "51.83%", "1,191,724", "46.98%", "1,080,113", "1.19%", "27,445"], ["Stanislaus", "53.64%", "30,431", "44.80%", "25,417", "1.57%", "888"], ["Calaveras", "46.37%", "2,379", "51.75%", "2,655", "1.87%", "96"], ["Placer", "59.98%", "13,592", "38.29%", "8,677", "1.72%", "390"], ["Sutter", "41.19%", "4,816", "57.59%", "6,734", "1.21%", "142"], ["Tehama", "51.36%", "5,077", "46.44%", "4,591", "2.21%", "218"], ["Kings", "59.03%", "9,141", "39.48%", "6,113", "1.49%", "231"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:which candidate lost in the county in which he received his highest number of votes? | Nixon | 128 | Answer: |
Table InputTable: [["Rank", "Player Name", "No. of Titles", "Runner-up", "Final Appearances"], ["1", "Jansher Khan", "8", "1", "9"], ["2", "Jahangir Khan", "6", "3", "9"], ["17", "Mohibullah Khan", "0", "1", "1"], ["8", "Peter Nicol", "1", "2", "3"], ["15", "Qamar Zaman", "0", "4", "4"], ["17", "Karim Darwish", "0", "1", "1"], ["17", "Ahmed Barada", "0", "1", "1"], ["6", "Ramy Ashour", "2", "1", "3"], ["3", "Geoff Hunt", "4", "1", "5"], ["17", "James Willstrop", "0", "1", "1"], ["17", "Mohamed El Shorbagy", "0", "1", "1"], ["12", "Rodney Martin", "1", "0", "1"], ["15", "Grégory Gaultier", "0", "4", "4"], ["9", "Rodney Eyles", "1", "1", "2"], ["17", "Lee Beachill", "0", "1", "1"], ["4", "Amr Shabana", "4", "0", "4"], ["6", "David Palmer", "2", "1", "3"], ["17", "Dean Williams", "0", "1", "1"], ["9", "Ross Norman", "1", "1", "2"], ["17", "Peter Marshall", "0", "1", "1"], ["9", "Thierry Lincou", "1", "1", "2"], ["14", "Chris Dittmar", "0", "5", "5"], ["12", "Jonathon Power", "1", "0", "1"], ["5", "Nick Matthew", "3", "0", "3"], ["17", "John White", "0", "1", "1"], ["17", "Del Harris", "0", "1", "1"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many titles has jansher khan won? | 8 | 128 | Answer: |
Table InputTable: [["Year", "Date", "Winner", "Result", "Loser", "Location"], ["2007", "January 7", "Philadelphia Eagles", "23-20", "New York Giants", "Lincoln Financial Field"], ["2009", "November 1", "Philadelphia Eagles", "40-17", "New York Giants", "Lincoln Financial Field"], ["2006", "September 17", "New York Giants", "30-24 (OT)", "Philadelphia Eagles", "Lincoln Financial Field"], ["2004", "September 12", "Philadelphia Eagles", "31-17", "New York Giants", "Lincoln Financial Field"], ["2003", "November 16", "Philadelphia Eagles", "28-10", "New York Giants", "Lincoln Financial Field"], ["2007", "December 9", "New York Giants", "16-13", "Philadelphia Eagles", "Lincoln Financial Field"], ["2008", "November 9", "New York Giants", "36-31", "Philadelphia Eagles", "Lincoln Financial Field"], ["2005", "December 11", "New York Giants", "26-23 (OT)", "Philadelphia Eagles", "Lincoln Financial Field"], ["2009", "January 11", "Philadelphia Eagles", "23-11", "New York Giants", "Giants Stadium"], ["2001", "January 7", "New York Giants", "20-10", "Philadelphia Eagles", "Giants Stadium"], ["2001", "October 22", "Philadelphia Eagles", "10-9", "New York Giants", "Giants Stadium"], ["2003", "October 19", "Philadelphia Eagles", "14-10", "New York Giants", "Giants Stadium"], ["2007", "September 30", "New York Giants", "16-3", "Philadelphia Eagles", "Giants Stadium"], ["2008", "December 7", "Philadelphia Eagles", "20-14", "New York Giants", "Giants Stadium"], ["2006", "December 17", "Philadelphia Eagles", "36-22", "New York Giants", "Giants Stadium"], ["2001", "December 30", "Philadelphia Eagles", "24-21", "New York Giants", "Veterans Stadium"], ["2004", "November 28", "Philadelphia Eagles", "27-6", "New York Giants", "Giants Stadium"], ["2002", "October 28", "Philadelphia Eagles", "17-3", "New York Giants", "Veterans Stadium"], ["2000", "October 29", "New York Giants", "24-7", "Philadelphia Eagles", "Giants Stadium"], ["2002", "December 28", "New York Giants", "10-7", "Philadelphia Eagles", "Giants Stadium"], ["2009", "December 13", "Philadelphia Eagles", "45-38", "New York Giants", "Giants Stadium"], ["2005", "November 20", "New York Giants", "27-17", "Philadelphia Eagles", "Giants Stadium"], ["2000", "September 10", "New York Giants", "33-18", "Philadelphia Eagles", "Veterans Stadium"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:in what year was the first game at lincoln financial field played between the eagles and giants? | 2003 | 128 | Answer: |
Table InputTable: [["Season", "Series", "Team", "Races", "Wins", "Poles", "F/Laps", "Podiums", "Points", "Position"], ["2011", "Formula Pilota China", "Asia Racing Team", "12", "2", "0", "0", "3", "124", "2nd"], ["2009", "Asian Formula Renault Challenge", "Asia Racing Team", "12", "6", "2", "4", "7", "287", "2nd"], ["2012", "British Formula 3 Championship", "Angola Racing Team", "5", "0", "0", "0", "0", "—", "—"], ["2012", "Formula 3 Euro Series", "Angola Racing Team", "21", "0", "0", "0", "0", "14", "14th"], ["2012", "Masters of Formula 3", "Angola Racing Team", "1", "0", "0", "0", "0", "—", "18th"], ["2010", "ATS Formel 3 Cup", "China Sonangol", "5", "0", "0", "0", "0", "0", "19th"], ["2009", "Formula Renault 2.0 Northern European Cup", "Krenek Motorsport", "14", "0", "0", "0", "0", "44", "21st"], ["2008", "Asian Formula Renault Challenge", "Champ Motorsports", "13", "0", "0", "0", "3", "193", "4th"], ["2007", "Asian Formula Renault Challenge", "Champ Motorsports", "12", "0", "0", "0", "1", "64", "14th"], ["2012", "59th Macau Grand Prix Formula 3", "Angola Racing Team", "2", "0", "0", "0", "0", "—", "23rd"], ["2010", "Austria Formula 3 Cup", "Sonangol Motopark", "4", "1", "2", "3", "2", "35", "9th"], ["2013", "GP3 Series", "Carlin", "16", "0", "0", "0", "0", "0", "23rd"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what is the total number of teams listed? | 7 | 128 | Answer: |
Table InputTable: [["Round", "Pick", "Player", "Position", "Nationality", "School/Club Team"], ["1", "15", "Reggie Johnson", "PF/C", "United States", "Tennessee"], ["3", "61", "Rich Yonakor", "", "United States", "North Carolina"], ["10", "209", "Steve Schall", "", "United States", "Arkansas"], ["3", "60", "Lavon Mercer", "", "United States", "Georgia"], ["8", "172", "Bill Bailey", "", "United States", "Texas Pan-American"], ["9", "192", "Al Williams", "", "United States", "North Texas State"], ["6", "129", "Dean Uthoff", "", "United States", "Iowa State"], ["5", "107", "Gib Hinz", "", "United States", "Wisconsin-Eau Claire"], ["7", "153", "Allan Zahn", "", "United States", "Arkansas"], ["4", "83", "Calvin Roberts", "", "United States", "California State-Fullerton"], ["2", "39", "Michael Miley", "", "United States", "California State-Long Beach"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:who was the top pick? | Reggie Johnson | 128 | Answer: |
Table InputTable: [["Season", "Tier", "Division", "Place"], ["1991/92", "3", "2ªB", "12th"], ["1989/90", "4", "3ª", "1st"], ["1997/98", "4", "3ª", "1st"], ["1992/93", "3", "2ªB", "4th"], ["1996/97", "4", "3ª", "2nd"], ["1988/89", "4", "3ª", "3rd"], ["1998/99", "4", "3ª", "6th"], ["1995/96", "3", "2ªB", "19th"], ["1990/91", "3", "2ªB", "6th"], ["1994/95", "3", "2ªB", "9th"], ["1993/94", "3", "2ªB", "15th"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:which division won first place? | 3ª | 128 | Answer: |
Table InputTable: [["Distribution", "x86", "x86-64", "ia64", "ppc", "ppc64", "sparc32", "sparc64", "arm", "hppa", "mips", "sh", "s390", "s390x", "alpha", "m68k"], ["Distribution", "x86", "x86-64", "ia64", "ppc", "ppc64", "sparc32", "sparc64", "arm", "hppa", "mips", "sh", "s390", "s390x", "alpha", "m68k"], ["MintPPC", "No", "No", "No", "Yes", "Yes", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No"], ["Red Hat Linux", "Yes", "No", "Discontinued\\n7.1-7.2", "Test release\\n5.1", "No", "Discontinued\\n4.0-4.2\\n5.1-6.2", "Test release\\n5.1", "No", "No", "Test release\\n5.1", "No", "Discontinued\\n7.2", "Discontinued\\n7.1", "Discontinued\\n2.1-7.1", "Test release\\n5.1"], ["Red Hat Enterprise Linux", "Discontinued\\n2.1-6", "Yes\\n3+", "Discontinued\\n2.1-5", "Yes\\n3+", "Yes\\n3+", "No", "No", "No", "No", "No", "No", "Discontinued\\n3-4", "Yes\\n3+", "No", "No"], ["XBMC", "Yes", "No", "No", "No", "No", "No", "No", "Yes", "No", "No", "No", "No", "No", "No", "No"], ["Slackware", "Yes", "Yes", "No", "No", "No", "Discontinued\\n?", "No", "Yes", "No", "No", "No", "Discontinued\\n?", "Discontinued\\n?", "Discontinued\\n8.1", "No"], ["Frugalware", "Yes", "Yes", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No"], ["Tor-ramdisk", "Yes", "Yes", "No", "No", "No", "No", "No", "No", "No", "Yes", "No", "No", "No", "No", "No"], ["CentOS", "Yes", "Yes", "Discontinued\\n3.5-3.8\\n4.1-4.7", "Beta\\n4.0", "No", "Beta\\n4.2", "No", "No", "No", "No", "No", "Discontinued\\n3.5-3.8\\n4.1-4.7", "Discontinued\\n3.5-3.8\\n4.1-4.7", "Discontinued\\n4.2-4.3", "No"], ["Fedora", "Yes", "Yes", "Discontinued from\\nFedora 9", "Yes", "Yes", "No", "Still active but slow in development, Last available is\\nFedora 12\\n, Working on\\nFedora 18", "Yes", "No", "Inactive from\\nFedora 13", "No", "No", "Yes", "No", "No"], ["Gentoo", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes"], ["Scientific Linux", "Yes", "Yes", "Discontinued\\n3-4", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No"], ["Finnix", "Yes", "Yes", "No", "Yes", "Yes", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No"], ["SUSE Linux Enterprise Server", "Yes", "Yes", "Yes", "Yes", "Yes", "No", "No", "No", "No", "No", "No", "No", "Yes", "No", "No"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:the total number of distributors that supply ppc | 13 | 128 | Answer: |
Table InputTable: [["#", "Massif", "Region", "Type of nature reserve", "Preserved area", "Buffer zone"], ["1", "Chornohora", "Zakarpattia", "Carpathian Biosphere Reserve", "2476.8 ha", "12925 ha"], ["5", "Kuziy / Trybushany", "Zakarpattia", "Carpathian Biosphere Reserve", "1369.6 ha", "3163.4 ha"], ["2", "Uholka / Wide Meadow", "Zakarpattia", "Carpathian Biosphere Reserve", "11860 ha", "3301 ha"], ["3", "Svydovets", "Zakarpattia", "Carpathian Biosphere Reserve", "3030.5 ha", "5639.5 ha"], ["4", "Maramoros", "Zakarpattia", "Carpathian Biosphere Reserve", "2243.6 ha", "6230.4 ha"], ["6", "Stuzhytsia / Uzhok", "Zakarpattia", "Uzh National Nature Park", "2532 ha", "3615 ha"], ["9", "Vihorlat", "Presov", "Presov Preserved areas", "2578 ha", "2413 ha"], ["10", "Havešová", "Presov", "Presov Preserved areas", "171.3 ha", "63.9 ha"], ["8", "Rožok", "Presov", "Presov Preserved areas", "67.1 ha", "41.4 ha"], ["11", "Jasmund", "Mecklenburg-Vorpommern", "Jasmund National Park", "492.5 ha", "2510.5 ha"], ["13", "Grumsiner Forest", "Brandenburg", "Grumsiner Forest Nature Reserve", "590.1 ha", "274.3 ha"], ["14", "Hainich", "Thuringia", "Hainich National Park", "1573.4 ha", "4085.4 ha"], ["12", "Serrahn", "Mecklenburg-Vorpommern", "Müritz National Park", "268.1 ha", "2568 ha"], ["15", "Kellerwald", "Hesse", "Kellerwald-Edersee National Park", "1467.1 ha", "4271.4 ha"], ["7", "Stužica / Bukovské vrchy", "Presov", "Poloniny National Park", "2950 ha", "11300 ha"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many massifs are in the zakarpattia region? | 6 | 128 | Answer: |
Table InputTable: [["Date", "Opponent#", "Rank#", "Site", "TV", "Result", "Attendance"], ["September 19", "at Iowa State*", "", "Cyclone Stadium • Ames, IA (Cy-Hawk Trophy)", "", "L 12-23", "53,922"], ["November 21", "Michigan State", "#19", "Kinnick Stadium • Iowa City, IA", "", "W 36-7", "60,103"], ["September 26", "#6 UCLA*", "", "Kinnick Stadium • Iowa City, IA", "", "W 20-7", "60,004"], ["September 12", "#7 Nebraska*", "", "Kinnick Stadium • Iowa City, IA", "", "W 10-7", "60,160"], ["November 7", "Purdue", "", "Kinnick Stadium • Iowa City, IA", "", "W 33-7", "60,114"], ["October 10", "Indiana", "#15", "Kinnick Stadium • Iowa City, IA", "", "W 42-28", "60,000"], ["October 24", "Minnesota", "#6", "Kinnick Stadium • Iowa City, IA (Floyd of Rosedale)", "ABC", "L 10-12", "60,000"], ["January 1", "vs. #12 Washington*", "#13", "Rose Bowl • Pasadena, CA (Rose Bowl)", "NBC", "L 0-28", "105,611"], ["October 31", "at Illinois", "#16", "Memorial Stadium • Champaign, IL", "", "L 7-24", "66,877"], ["October 3", "at Northwestern", "#18", "Dyche Stadium • Evanston, IL", "", "W 64-0", "30,113"], ["October 17", "at #5 Michigan", "#12", "Michigan Stadium • Ann Arbor, MI", "", "W 9-7", "105,915"], ["November 14", "at Wisconsin", "", "Camp Randall Stadium • Madison, WI", "ABC", "W 17-7", "78,731"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what is the total number of points that the iowa hawkeyes scored against perdue? | 33 | 128 | Answer: |
Table InputTable: [["Week", "Date", "Opponent", "Score", "Result", "Record"], ["5", "Sept 25", "vs. Hamilton Tiger-Cats", "38–12", "Loss", "1–5"], ["4", "Sept 18", "vs. Toronto Argonauts", "34–6", "Loss", "1–4"], ["12", "Nov 13", "vs. Montreal Alouettes", "14–12", "Win", "2–12"], ["10", "Oct 30", "vs. Hamilton Tiger-Cats", "30–9", "Loss", "1–11"], ["8", "Oct 16", "vs. Toronto Argonauts", "27–11", "Loss", "1–9"], ["2", "Sept 6", "vs. Montreal Alouettes", "20–11", "Loss", "0–3"], ["3", "Sept 11", "at Toronto Argonauts", "12–5", "Win", "1–3"], ["7", "Oct 9", "vs. Montreal Alouettes", "25–11", "Loss", "1–7"], ["2", "Sept 4", "at Montreal Alouettes", "21–2", "Loss", "0–2"], ["6", "Oct 2", "at Hamilton Tiger-Cats", "45–0", "Loss", "1–6"], ["1", "Aug 28", "at Toronto Argonauts", "13–6", "Loss", "0–1"], ["11", "Nov 6", "at Toronto Argonauts", "18–12", "Loss", "1–12"], ["9", "Oct 23", "at Hamilton Tiger-Cats", "25–17", "Loss", "1–10"], ["7", "Oct 11", "at Montreal Alouettes", "24–6", "Loss", "1–8"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what is the highest amount of points scored in a game by an opponent? | 45 | 128 | Answer: |
Table InputTable: [["Hand", "Auto", "Wind", "Athlete", "Nationality", "Birthdate", "Location", "Date"], ["", "10.95", "", "Karl Heinz Schröder", "Germany", "17.06.1939", "Hannover", "28.07.1979"], ["", "10.84", "1.8", "Erik Oostweegel", "Netherlands", "29.04.1960", "Tilburg", "10.06.2000"], ["", "10.26", "2.3", "Troy Douglas", "Netherlands", "30.11.1962", "Utrecht", "10.07.2004"], ["10.7", "", "", "Klaus Jürgen Schneider", "Germany", "02.03.1942", "Stuttgart", "07.07.1982"], ["", "10.93", "0.6", "Gilles Echevin", "France", "01.09.1948", "Grenoble", "07.05.1989"], ["", "10.29", "1.9", "Troy Douglas", "Netherlands", "30.11.1962", "Leiden", "07.06.2003"], ["", "10.90", "", "Thaddeus Bell", "United States", "28.11.1942", "Raleigh", "01.05.1988"], ["10.7", "", "", "Thane Baker", "United States", "04.10.1931", "Elkhart", "13.09.1972"], ["", "10.95", "", "George McNeill", "United Kingdom", "19.02.1947", "Melbourne", "31.11.1987"], ["", "10.87", "", "Eddie Hart", "United States", "24.04.1949", "Eugene", "03.08.1989"], ["10.7", "", "", "Walt Butler", "United States", "21.03.1941", "Northridge", "16.05.1981"], ["", "10.60", "", "Bill Collins", "United States", "20.11.1950", "", "06.06.1992"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:name an athlete from the same country as schroder. | Klaus Jürgen Schneider | 128 | Answer: |
Table InputTable: [["Year", "Competition", "Venue", "Position", "Event"], ["1997", "USA Outdoor Championships", "Indianapolis, United States", "1st", "100 m hurdles"], ["2000", "Olympic Games", "Sydney, Australia", "3rd", "100 m hurdles"], ["1998", "USA Indoor Championships", "", "1st", "60 m hurdles"], ["1997", "World Indoor Championships", "Paris, France", "5th", "60 m hurdles"], ["2002", "USA Indoor Championships", "", "1st", "60 m hurdles"], ["2003", "World Indoor Championships", "Birmingham, England", "3rd", "60 m hurdles"], ["2003", "World Athletics Final", "Monaco", "6th", "100 m hurdles"], ["2000", "Grand Prix Final", "Doha, Qatar", "4th", "100 m hurdles"], ["2004", "Olympic Games", "Athens, Greece", "3rd", "100 m hurdles"], ["1999", "World Indoor Championships", "Maebashi, Japan", "6th", "60 m hurdles"], ["2002", "Grand Prix Final", "Paris, France", "7th", "100 m hurdles"], ["1998", "Grand Prix Final", "Moscow, Russia", "2nd", "100 m hurdles"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many events did melissa morrison-howard finish first in? | 3 | 128 | Answer: |
Table InputTable: [["Year", "Title", "Role", "Notes"], ["2012", "Christmas Angel", "Daphney", ""], ["2013", "The Real", "Herself", "Host"], ["1996", "All That", "Herself", ""], ["1999", "Detention", "Orangejella LaBelle", "13 episodes"], ["1998", "Blues Clues", "Herself", "1 episode"], ["1994–1999", "Sister, Sister", "Tamera Campbell", "119 episodes"], ["1995", "Are You Afraid of the Dark?", "Evil Chameleon", "1 episode"], ["1991", "Flesh'n Blood", "Penelope", "1 episode"], ["1992", "True Colors", "Lorae", "1 episode"], ["2011", "CHRISJayify", "Herself", "Episode: \"Drugs Are Bad\""], ["2014", "Melissa and Joey", "Gillian", "Season 3 Episode 24 'To Tell the Truth'"], ["2000", "How I Loved a Macho Boy", "Jamal Santos", "3 episodes"], ["1995–1996", "The Adventures of Hyperman", "Emma C. Squared", "8 episodes"], ["2011", "Things We Do for Love", "Lourdes", "5 episodes"], ["2006–2007", "Family Guy", "Esther", "Voice\\n3 episodes"], ["2004–2006", "Strong Medicine", "Dr. Kayla Thorton", "37 episodes"], ["1997", "Smart Guy", "Roxanne", "1 episode"], ["1994", "The All-New Mickey Mouse (MMC)", "Herself", "1 episode"], ["2009", "Roommates", "Hope", "13 episodes"], ["2009", "The Super Hero Squad Show", "Misty Knight", "1 episode"], ["2011", "Access Hollywood Live", "Herself", "Co-host"], ["2011–2013", "Tia & Tamera", "Herself", "Executive producer"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what is the name of the last title? | Melissa and Joey | 128 | Answer: |
Table InputTable: [["District", "Incumbent", "Party", "First\\nelected", "Result", "Candidates"], ["Maryland 8", "Connie Morella", "Republican", "1986", "Lost re-election\\nDemocratic gain", "Chris Van Hollen (D) 51.71%\\nConnie Morella (R) 47.49%\\nStephen Bassett (UN) 0.73%"], ["Maryland 6", "Roscoe Bartlett", "Republican", "1992", "Re-elected", "Roscoe Bartlett (R) 66.11%\\nDonald DeArmon (D) 33.80%"], ["Maryland 1", "Wayne Gilchrest", "Republican", "1990", "Re-elected", "Wayne Gilchrest (R) 76.67%\\nAnn Tamlyn (D) 23.16%"], ["Maryland 4", "Albert Wynn", "Democratic", "1992", "Re-elected", "Albert Wynn (D) 78.57%\\nJohn Kimble (R) 20.82%"], ["Maryland 3", "Ben Cardin", "Democratic", "1986", "Re-elected", "Ben Cardin (D) 65.72%\\nScott Conwell (R) 34.18%"], ["Maryland 5", "Steny Hoyer", "Democratic", "1981", "Re-elected", "Steny Hoyer (D) 69.27%\\nJoseph Crawford (R) 30.52%"], ["Maryland 2", "Robert Ehrlich", "Republican", "1994", "Retired to run for Governor\\nDemocratic gain", "Dutch Ruppersberger (D) 54.16%\\nHelen Bentley (R) 45.57%"], ["Maryland 7", "Elijah Cummings", "Democratic", "1996", "Re-elected", "Elijah Cummings (D) 73.53%\\nJoseph Ward (R) 26.38%"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:connie morella ran for re-election, but lost to which candidate? | Chris Van Hollen | 128 | Answer: |
Table InputTable: [["Representative", "Years", "State", "Party", "Lifespan"], ["John A. Kasson", "1863–1867\\n1873–1877\\n1881–1884", "Iowa", "Republican", "1822–1910"], ["Martin Kalbfleisch", "1863–1865", "New York", "Democratic", "1804–1873"], ["Steve Kagen", "2007–2011", "Wisconsin", "Democratic", "1949–"], ["Raymond W. Karst", "1949–1951", "Missouri", "Democratic", "1902–1987"], ["John H. Ketcham", "1865–1873\\n1877–1893\\n1897–1906", "New York", "Republican", "1832–1906"], ["Robert M. Knapp", "1873–1875\\n1877–1879", "Illinois", "Democratic", "1831–1889"], ["Bob Kasten", "1975–1979", "Wisconsin", "Republican", "1942–"], ["William Henry Kurtz", "1851–1855", "Pennsylvania", "Democratic", "1804–1868"], ["John Kean", "1883–1885\\n1887–1889", "New Jersey", "Republican", "1852–1914"], ["John C. Kunkel", "1939–1951\\n1961–1966", "Pennsylvania", "Republican", "1898–1970"], ["Robert Kastenmeier", "1959–1991", "Wisconsin", "Democratic", "1924–"], ["Charles J. Kersten", "1947–1949\\n1951–1955", "Wisconsin", "Republican", "1902–1972"], ["John Weinland Killinger", "1859–1863\\n1871–1875\\n1877–1881", "Pennsylvania", "Republican", "1824–1896"], ["Aaron Shenk Kreider", "1913–1923", "Pennsylvania", "Republican", "1863–1929"], ["Jacob Banks Kurtz", "1923–1935", "Pennsylvania", "Republican", "1867–1960"], ["Frank Bateman Keefe", "1939–1951", "Wisconsin", "Republican", "1887–1952"], ["William H. Kelsey", "1857–1859\\n1867–1871", "New York", "Republican", "1812–1879"], ["Charles Knapp", "1869–1871", "New York", "Republican", "1797–1880"], ["Charles Edward Kiefner", "1925–1927\\n1929–1931", "Missouri", "Republican", "1869–1942"], ["Julius Kahn", "1899–1903\\n1905–1924", "California", "Republican", "1861–1924"], ["Andrew Kiefer", "1893–1897", "Minnesota", "Republican", "1832–1904"], ["Philip Knopf", "1903–1909", "Illinois", "Republican", "1847–1920"], ["Marcus C.L. Kline", "1903–1907", "Pennsylvania", "Democratic", "1855–1911"], ["J. Warren Keifer", "1877–1885\\n1905–1911", "Ohio", "Republican", "1836–1932"], ["Isaac Clinton Kline", "1921–1923", "Pennsylvania", "Republican", "1858–1947"], ["John Christian Kunkel", "1857–1859", "Pennsylvania", "Republican", "1816–1870"], ["Will Kirk Kaynor", "1929", "Massachusetts", "Republican", "1884–1929"], ["Chauncey L. Knapp", "1857–1859", "Massachusetts", "Republican", "1809–1898"], ["Charles J. Knapp", "1889–1891", "New York", "Republican", "1845–1916"], ["Peter H. Kostmayer", "1977–1981\\n1983–1993", "Pennsylvania", "Democratic", "1946–"], ["James Kennedy", "1903–1911", "Ohio", "Republican", "1853–1928"], ["Frank B. Klepper", "1905–1907", "Missouri", "Republican", "1864–1933"], ["George Kremer", "1823–1825", "Pennsylvania", "Democratic-Republican", "1775–1854"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:name someone from the same state and party as karst. | Frank M. Karsten | 128 | Answer: |
Table InputTable: [["Year", "Competition", "Venue", "Position", "Event", "Notes"], ["2007", "World Championships", "Osaka, Japan", "3rd", "400 m hurdles", "48.12 (NR)"], ["2008", "Olympic Games", "Beijing, China", "6th", "400 m hurdles", "48.42"], ["2001", "World Championships", "Edmonton, Canada", "18th (sf)", "400 m hurdles", "49.80"], ["2006", "European Championships", "Gothenburg, Sweden", "2nd", "400 m hurdles", "48.71"], ["2012", "European Championships", "Helsinki, Finland", "18th (sf)", "400 m hurdles", "50.77"], ["2004", "Olympic Games", "Athens, Greece", "6th", "400 m hurdles", "49.00"], ["2001", "Universiade", "Beijing, China", "8th", "400 m hurdles", "49.68"], ["2000", "World Junior Championships", "Santiago, Chile", "1st", "400 m hurdles", "49.23"], ["2003", "World Indoor Championships", "Birmingham, United Kingdom", "7th (sf)", "400 m", "46.82"], ["1999", "European Junior Championships", "Riga, Latvia", "4th", "400 m hurdles", "52.17"], ["2003", "European U23 Championships", "Bydgoszcz, Poland", "1st", "400 m hurdles", "48.45"], ["2002", "European Championships", "Munich, Germany", "4th", "400 m", "45.40"], ["2002", "European Indoor Championships", "Vienna, Austria", "1st", "400 m", "45.39 (CR, NR)"], ["2007", "World Championships", "Osaka, Japan", "3rd", "4x400 m relay", "3:00.05"], ["2003", "World Indoor Championships", "Birmingham, United Kingdom", "3rd", "4x400 m relay", "3:06.61"], ["2008", "Olympic Games", "Beijing, China", "7th", "4x400 m relay", "3:00.32"], ["2002", "European Indoor Championships", "Vienna, Austria", "1st", "4x400 m relay", "3:05.50 (CR)"], ["2002", "European Championships", "Munich, Germany", "8th", "4x400 m relay", "DQ"], ["2004", "Olympic Games", "Athens, Greece", "10th (h)", "4x400 m relay", "3:03.69"], ["2003", "European U23 Championships", "Bydgoszcz, Poland", "1st", "4x400 m relay", "3:03.32"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what are the number of times the 400 m hurdles was listed as the event? | 10 | 128 | Answer: |
Table InputTable: [["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"], ["5", "North Korea", "1", "0", "1", "2"], ["6", "South Korea", "0", "0", "2", "2"], ["2", "Japan", "7", "10", "7", "24"], ["1", "China", "13", "9", "13", "35"], ["3", "Uzbekistan", "1", "2", "3", "6"], ["4", "Kazakhstan", "2", "2", "0", "4"], ["Total", "Total", "24", "23", "26", "73"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what is the average number of gold medals won by china, japan, and north korea? | 7 | 128 | Answer: |
Table InputTable: [["Pos", "Name", "Team", "Laps", "Time/Retired", "Grid", "Points"], ["3", "Bruno Junqueira", "Dale Coyne Racing", "69", "+8.419", "11", "26"], ["12", "Katherine Legge", "Dale Coyne Racing", "69", "+44.860", "14", "9"], ["13", "Robert Doornbos", "Minardi Team USA", "69", "+1:00.638", "9", "8"], ["11", "Dan Clarke", "Minardi Team USA", "69", "+38.903", "10", "11"], ["15", "Alex Tagliani", "Rocketsports", "68", "+ 1 Lap", "12", "6"], ["6", "Simon Pagenaud", "Team Australia", "69", "+22.698", "5", "19"], ["2", "Jan Heylen", "Conquest Racing", "69", "+7.227", "7", "27"], ["16", "Alex Figge", "Pacific Coast Motorsports", "68", "+ 1 Lap", "16", "5"], ["8", "Oriol Servià", "Forsythe Racing", "69", "+23.406", "13", "15"], ["5", "Neel Jani", "PKV Racing", "69", "+22.262", "4", "21"], ["7", "Sébastien Bourdais", "N/H/L Racing", "69", "+22.955", "1", "18"], ["4", "Tristan Gommendy", "PKV Racing", "69", "+9.037", "3", "23"], ["1", "Justin Wilson", "RuSPORT", "69", "1:46:02.236", "2", "32"], ["14", "Will Power", "Team Australia", "69", "+1:01.204", "8", "7"], ["17", "Paul Tracy", "Forsythe Racing", "14", "Mechanical", "17", "4"], ["10", "Ryan Dalziel", "Pacific Coast Motorsports", "69", "+29.554", "15", "11"], ["9", "Graham Rahal", "N/H/L Racing", "69", "+23.949", "6", "13"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:who finished with more points, legge or junqueira? | Bruno Junqueira | 128 | Answer: |
Table InputTable: [["Year", "Competition", "Venue", "Position", "Event"], ["1999", "All-Africa Games", "Johannesburg, South Africa", "1st", "3000 m st."], ["1998", "World Cross Country Championships", "Marrakech, Morocco", "8th", "Short race"], ["1996", "World Junior Championships", "Sydney, Australia", "2nd", "3000 m st."], ["2005", "World Athletics Final", "Monte Carlo, Monaco", "8th", "3000 m st."], ["2001", "IAAF Grand Prix Final", "Melbourne, Australia", "4th", "3000 m st."], ["", "IAAF Grand Prix Final", "Munich, Germany", "4th", "3000 m st."], ["2004", "World Athletics Final", "Monte Carlo, Monaco", "4th", "3000 m st."], ["", "Commonwealth Games", "Kuala Lumpur, Malaysia", "3rd", "3000 m st."]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:in what competition did kipkurui misoi come in first? | All-Africa Games | 128 | Answer: |
Table InputTable: [["Wrestler:", "Reigns:", "Date:", "Place:", "Notes:"], ["Invader I", "5", "December 25, 1991", "San Juan, Puerto Rico", ""], ["Rico Suave", "2", "March 17, 2007", "Bayamon, Puerto Rico", ""], ["Invader I", "1", "November 5, 1986", "San Juan, Puerto Rico", ""], ["Rico Suave", "1", "April 6, 2002", "Caguas, Puerto Rico", ""], ["Invader I", "2", "September 18, 1987", "San Juan, Puerto Rico", ""], ["Ray Gonzalez", "1", "April 27, 2002", "San Lorenzo, Puerto Rico", ""], ["Chris Joel", "1", "May 10, 2008", "Bayamon, Puerto Rico", ""], ["Super Gladiator", "1", "October 6, 2001", "Caguas, Puerto Rico", ""], ["Hammett", "1", "March 1, 2008", "Tao Baja, Puerto Rico", ""], ["TNT", "3", "March 30, 1991", "Bayamon, Puerto Rico", ""], ["TNT", "4", "June 1, 1991", "Bayamon, Puerto Rico", ""], ["B.J.", "2", "March 15, 2008", "Lares, Puerto Rico", ""], ["Carlos Colon", "1", "August 20, 1988", "Bayamon, Puerto Rico", ""], ["TNT", "5", "October 26, 1991", "Carolina, Puerto Rico", ""], ["Crazy Rudy", "1", "April 28, 2007", "Bayamon, Puerto Rico", ""], ["Vengador Boricua", "1", "July 19, 2003", "Carolina, Puerto Rico", "title becomes inactive when Vengador Boricua leaves the company."], ["Rex King", "3", "March 19, 2000", "Cabo Rojo, Puerto Rico", ""], ["Invader I", "4", "April 2, 1988", "Bayamon, Puerto Rico", ""], ["Fidel Sierra", "1", "October 19, 1991", "Bayamon, Puerto Rico", ""], ["TNT", "2", "April 25, 1990", "San Juan, Puerto Rico", "Won the vacant title"], ["El Bronco I", "1", "May 18, 1996", "Caguas, Puerto Rico", ""], ["Carlos Colon", "4", "June 8, 2002", "Toa Baja, Puerto Rico", ""], ["Fidel Sierra", "2", "August 24, 2002", "Coamo, Puerto Rico", ""], ["Alex Porteau", "1", "July 7, 2001", "Carolina, Puerto Rico", ""], ["Chris Candido", "1", "June 6, 2003", "Cayey, Puerto Rico", ""], ["\"Jungle\" Jim Steele", "1", "April 20, 1996", "Caguas, Puerto Rico", ""], ["Chris Grant", "1", "April 21, 2001", "Orocovis, Puerto Rico", ""], ["Mustafa Saed", "1", "August 14, 1999", "Caguas, Puerto Rico", ""], ["Super Black Ninja", "1", "February 6, 1988", "Guaynabo, Puerto Rico", ""], ["Dick Murdoch", "1", "November 23, 1991", "Arroyo, Puerto Rico", ""], ["Bad Boy Bradley", "1", "September 8, 2001", "Bayamón, Puerto Rico", ""], ["Grizzly Boone", "1", "October 24, 1987", "Bayamon, Puerto Rico", ""], ["Ron Starr", "2", "June 25, 1988", "Carolina, Puerto Rico", ""], ["Ricky Santana", "2", "March 23, 1996", "Caguas, Puerto Rico", ""], ["Sweet Brown Sugar (Skip Young)", "1", "January 6, 1996", "Caguas, Puerto Rico", ""], ["Carlos Colon", "3", "June 18, 1994", "San Juan, Puerto Rico", "Defeated Mighty Koadiak in a tournament final."], ["Glamour Boy Shane", "1", "April 2, 1999", "Guaynabo, Puerto Rico", "Defeated \"Jungle\" Jim Steele for vacant title."]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many times does san juan, puerto rico appear on this chart? | 8 | 128 | Answer: |
Table InputTable: [["Rank", "Name", "Nationality", "Time", "Notes", "Points"], ["4", "Mario Scapini", "Italy", "1:47.20", "PB", "9"], ["12", "Milan Kocourek", "Czech Republic", "1:59.28", "", "1"], ["8", "Robin Schembera", "Germany", "1:47.79", "", "5"], ["1", "Adam Kszczot", "Poland", "1:46.50", "", "12"], ["11", "António Rodrigues", "Portugal", "1:50.45", "", "2"], ["9", "Ivan Tukhtachev", "Russia", "1:48.27", "SB", "4"], ["2", "Jeff Lastennet", "France", "1:46.70", "", "11"], ["10", "Antonio Manuel Reina", "Spain", "1:48.56", "", "3"], ["3", "Gareth Warburton", "Great Britain", "1:46.95", "SB", "10"], ["6", "Oleh Kayafa", "Ukraine", "1:47.42", "", "7"], ["5", "Anis Ananenka", "Belarus", "1:47.29", "", "8"], ["7", "Joni Jaako", "Sweden", "1:47.61", "SB", "6"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:who holds the first fastest time? | Adam Kszczot | 128 | Answer: |
Table InputTable: [["Callsign", "Area served", "Frequency", "Band", "Fate", "Freq currently", "Purpose"], ["7CAE", "Hobart", "092.1", "FM", "Changed call to 7THE ca. 1980", "7THE", "Community"], ["7UV", "Ulverstone", "", "AM", "Moved to Devonport and changed call to 7AD in 1940", "7AD", "Commercial"], ["7QT", "Queenstown", "0837", "AM", "Changed call to 7XS in 1988", "7XS", "Commercial"], ["7ZL", "Hobart", "0603", "AM", "Changed call to 7RN in 1991", "7RN", "National"], ["7NT", "Launceston", "0711", "AM", "Moved to FM in 2006, retained call", "silent", "National"], ["7LA", "Launceston", "1098", "AM", "Moved to FM in 2008 as 7LAA", "silent", "Commercial"], ["7EX", "Launceston", "1008", "AM", "Moved to FM in 2008 as 7EXX", "silent", "Commercial"], ["7HO", "Hobart", "0864", "AM", "Moved to FM in 1990 as 7HHO", "7RPH", "Commercial"], ["7DY", "Derby", "", "AM", "Moved to Scottsdale and changed call to 7SD in 1954", "7SD", "Commercial"], ["7QN", "Queenstown", "0630", "AM", "Moved to FM in 1991, retained call", "7RN", "National"], ["7HT", "Hobart", "1080", "AM", "Moved to FM in 1998 as 7XXX", "7TAB (HPON)", "Commercial"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what is the callsign of the only community station? | 7CAE | 128 | Answer: |
Table InputTable: [["No", "Driver", "Entrant", "Constructor", "Chassis", "Engine"], ["24", "Heinrich Joachim von Morgen", "Private entry", "Bugatti", "Bugatti T35B", "2.3 L8"], ["?", "William Grover-Williams", "Automobiles Ettore Bugatti", "Bugatti", "Bugatti T35B", "2.3 L8"], ["30", "Guy Bouriat", "Automobiles Ettore Bugatti", "Bugatti", "Bugatti T35B", "2.3 L8"], ["32", "Cesare Renzi", "Private entry", "Bugatti", "Bugatti T37A", "1.5 L8"], ["26", "Emil Frankl", "Private entry", "Steyr", "Steyr 4.5 Liter", "4.5 L6"], ["?", "Arrigo Nenzioni", "A. or E. Nenzioni", "Maserati", "Maserati 26", "1.5 L8"], ["6", "Clemente Biondetti", "Scuderia Materassi", "Talbot", "Talbot 700", "1.5 L8"], ["4", "Louis Chiron", "Automobiles Ettore Bugatti", "Bugatti", "Bugatti T35B", "2.3 L8"], ["34", "Cesare Pastore", "Private entry", "Maserati", "Maserati 26B", "2.1 L8"], ["10", "Cleto Nenzioni", "Private entry", "Maserati", "Maserati 26B", "2.1 L8"], ["12", "Luigi Fagioli", "Officine Alfieri Maserati", "Maserati", "Maserati 26", "1.7 L8"], ["?", "Filippo Sartorio", "Enrico or F. Sartorio", "Alfa Romeo", "Alfa Romeo 6C 1750", "1.5 L6"], ["20", "Arrigo Sartorio", "Private entry", "Maserati", "Maserati 26", "1.5 L8"], ["?", "?", "Scuderia Ferrari", "Alfa Romeo", "Alfa Romeo P2", "2.0 L8"], ["14", "Fritz Caflisch", "Private entry", "Mercedes-Benz", "Mercedes-Benz SS", "7.1 L6"], ["2", "Luigi Arcangeli", "Officine Alfieri Maserati", "Maserati", "Maserati 26M", "2.5 L8"], ["?", "?", "Scuderia Ferrari", "Alfa Romeo", "Alfa Romeo 6C 1500", "1.5 L6"], ["8", "Pietro Nicolotti", "Private entry", "Alfa Romeo", "Alfa Romeo 6C 1500", "1.5 L6"], ["16", "Achille Varzi", "SA Alfa Romeo", "Alfa Romeo", "Alfa Romeo P2", "2.0 L8"], ["28", "Tazio Nuvolari", "SA Alfa Romeo", "Alfa Romeo", "Alfa Romeo P2", "2.0 L8"], ["36", "Colonna de Stigliano", "Private entry", "Alfa Romeo", "Alfa Romeo 6C 1750", "1.8 L6"], ["18", "Giuseppe Campari", "Scuderia Ferrari", "Alfa Romeo", "Alfa Romeo 6C 1750GS", "1.8 L6"], ["22", "Mario Tadini", "Scuderia Ferrari", "Alfa Romeo", "Alfa Romeo 6C 1750GS", "1.8 L6"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:which constructor shows up the most on this list? | Alfa Romeo | 128 | Answer: |
Table InputTable: [["Pos", "Team", "Played", "Won", "Draw", "Lost", "Goals For", "Goals Against", "Goal Difference", "Points", "Notes"], ["1", "KR", "18", "11", "4", "3", "27", "14", "+13", "37", "UEFA Champions League"], ["3", "Grindavík", "18", "8", "6", "4", "25", "18", "+7", "30", "UEFA Cup"], ["2", "Fylkir", "18", "10", "5", "3", "39", "16", "+23", "35", "UEFA Cup"], ["4", "ÍBV", "18", "8", "5", "5", "29", "17", "+12", "29", "Inter-Toto Cup"], ["6", "Keflavík", "18", "4", "7", "7", "21", "35", "-14", "19", ""], ["10", "Leiftur", "18", "3", "7", "8", "24", "39", "-15", "16", "Relegated"], ["9", "Stjarnan", "18", "4", "5", "9", "18", "31", "-13", "17", "Relegated"], ["7", "Breiðablik", "18", "5", "3", "10", "29", "35", "-6", "18", ""], ["8", "Fram", "18", "4", "5", "9", "22", "33", "-11", "17", ""], ["5", "ÍA", "18", "7", "5", "6", "21", "17", "+4", "26", ""]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many goals did kr score? | 27 | 128 | Answer: |
Table InputTable: [["Season", "Team", "Country", "Division", "Apps", "Goals"], ["2006", "Spartak Nizhny Novgorod", "Russia", "2", "36", "1"], ["2012/13", "Lokomotiv Moscow", "Russia", "1", "8", "0"], ["2005", "CSKA Moscow", "Russia", "1", "0", "0"], ["2013/14", "Lokomotiv Moscow", "Russia", "1", "14", "1"], ["2004", "CSKA Moscow", "Russia", "1", "0", "0"], ["2011/12", "Dnipro Dnipropetrovsk", "Ukraine", "1", "16", "0"], ["2007/08", "Dnipro Dnipropetrovsk", "Ukraine", "1", "24", "0"], ["2010/11", "Dnipro Dnipropetrovsk", "Ukraine", "1", "23", "0"], ["2009/10", "Dnipro Dnipropetrovsk", "Ukraine", "1", "28", "0"], ["2012/13", "Dnipro Dnipropetrovsk", "Ukraine", "1", "10", "1"], ["2008/09", "Dnipro Dnipropetrovsk", "Ukraine", "1", "22", "1"], ["2006/07", "Dnipro Dnipropetrovsk", "Ukraine", "1", "12", "0"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what was the number of apps for spartak nizhy novgorod? | 36 | 128 | Answer: |
Table InputTable: [["Rank", "Player", "Nation", "Club", "Goals"], ["1", "Jhonny Arteaga", "COL", "FC New York", "13"], ["2", "Matthew Delicâte", "ENG", "Richmond Kickers", "10"], ["9", "George Davis IV", "USA", "Dayton Dutch Lions", "7"], ["6", "Andriy Budnyy", "UKR", "Wilmington Hammerheads", "8"], ["3", "José Angulo", "USA", "Harrisburg City Islanders", "9"], ["9", "Chris Banks", "USA", "Wilmington Hammerheads", "7"], ["3", "Luke Mulholland", "ENG", "Wilmington Hammerheads", "9"], ["9", "Sallieu Bundu", "SLE", "Charlotte Eagles", "7"], ["6", "Andrew Welker", "USA", "Harrisburg City Islanders", "8"], ["3", "Maxwell Griffin", "USA", "Orlando City", "9"], ["6", "Jamie Watson", "USA", "Orlando City", "8"], ["9", "Sainey Touray", "GAM", "Harrisburg City Islanders", "7"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:who earned the most goals? | Jhonny Arteaga | 128 | Answer: |
Table InputTable: [["Match", "Date", "Venue", "Opponents", "Score"], ["GL-A-1", "2006..", "[[]]", "[[]]", "-"], ["GL-A-2", "2006..", "[[]]", "[[]]", "-"], ["GL-A-3", "2006..", "[[]]", "[[]]", "-"], ["GL-A-4", "2006..", "[[]]", "[[]]", "-"], ["GL-A-5", "2006..", "[[]]", "[[]]", "-"], ["GL-A-6", "2006..", "[[]]", "[[]]", "-"], ["Quarterfinals-1", "2006..", "[[]]", "[[]]", "-"], ["Quarterfinals-2", "2006..", "[[]]", "[[]]", "-"], ["Semifinals-1", "2006..", "[[]]", "[[]]", "-"], ["Semifinals-2", "2006..", "[[]]", "[[]]", "-"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what was the year of the only gl-a-1 match? | 2006 | 128 | Answer: |
Table InputTable: [["Round", "Pick", "Player", "Position", "Nationality", "School/Club Team"], ["1", "15", "Reggie Johnson", "PF/C", "United States", "Tennessee"], ["10", "209", "Steve Schall", "", "United States", "Arkansas"], ["6", "129", "Dean Uthoff", "", "United States", "Iowa State"], ["4", "83", "Calvin Roberts", "", "United States", "California State-Fullerton"], ["3", "60", "Lavon Mercer", "", "United States", "Georgia"], ["8", "172", "Bill Bailey", "", "United States", "Texas Pan-American"], ["7", "153", "Allan Zahn", "", "United States", "Arkansas"], ["2", "39", "Michael Miley", "", "United States", "California State-Long Beach"], ["9", "192", "Al Williams", "", "United States", "North Texas State"], ["5", "107", "Gib Hinz", "", "United States", "Wisconsin-Eau Claire"], ["3", "61", "Rich Yonakor", "", "United States", "North Carolina"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:who was the first pick in the 1980 season draft? | Reggie Johnson | 128 | Answer: |
Table InputTable: [["Contestant", "Age", "Height", "Home City", "Rank"], ["Dzejlana \"Lana\" Baltić", "20", "179 cm (5 ft 10.5 in)", "Graz (originally from Bosnia)", "1st Eliminated in Episode 10"], ["Melisa Popanicić", "16", "175 cm (5 ft 9 in)", "Wörgl", "2nd Eliminated in Episode 10"], ["Sabrina Angelika Rauch †", "21", "175 cm (5 ft 9 in)", "Graz", "Eliminated in Episode 2"], ["Gina Zeneb Adamu", "17", "175 cm (5 ft 9 in)", "Bad Vöslau", "Runner-Up"], ["Christine Riener", "20", "181 cm (5 ft 11.25 in)", "Bludenz", "Eliminated in Episode 4"], ["Isabelle Raisa", "16", "170 cm (5 ft 7 in)", "Vienna", "Eliminated in Episode 1"], ["Alina Chlebecek", "18", "170 cm (5 ft 7 in)", "Deutsch-Wagram", "Eliminated in Episode 1"], ["Nadine Trinker", "21", "183 cm (6 ft 0 in)", "Bodensdorf", "Eliminated in Episode 9"], ["Bianca Ebelsberger", "24", "179 cm (5 ft 10.5 in)", "Aurach am Hongar", "Eliminated in Episode 9"], ["Katharina Mihalović", "23", "179 cm (5 ft 10.5 in)", "Vienna", "Eliminated in Episode 2"], ["Yemisi Rieger", "17", "177 cm (5 ft 9.5 in)", "Vienna", "Eliminated in Episode 7"], ["Nataša Marić", "16", "175 cm (5 ft 9 in)", "Liefering (originally from Serbia)", "Eliminated in Episode 3"], ["Teodora-Mădălina Andreica", "17", "177 cm (5 ft 9.5 in)", "Romania", "Eliminated in Episode 6"], ["Izabela Pop Kostić", "20", "170 cm (5 ft 7 in)", "Vienna (originally from Bosnia)", "Eliminated in Episode 8"], ["Antonia Maria Hausmair", "16", "175 cm (5 ft 9 in)", "Siegendorf", "Winner"], ["Michaela Schopf", "21", "172 cm (5 ft 7.5 in)", "Salzburg (originally from Germany)", "Quit in Episode 4"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:which contestants are below age 18? | Isabelle Raisa, Nataša Marić, Teodora-Mădălina Andreica, Yemisi Rieger, Melisa Popanicić, Gina Zeneb Adamu, Antonia Maria Hausmair | 128 | Answer: |
Table InputTable: [["Name", "Sport", "Event", "Placing", "Performance"], ["David Berger", "Weightlifting", "Light-heavyweight <82.5 kg", "—", "J:132.5 C:122.5 S:— T:—"], ["Ze'ev Friedman", "Weightlifting", "Bantamweight <56 kg", "12", "J:102.5 C:102.5 S:125 T:330"], ["Yossef Romano", "Weightlifting", "Middleweight <75 kg", "—", "(retired injured on third attempt to press 137.5kg)"], ["Eliezer Halfin", "Wrestling", "Freestyle — Lightweight <68 kg", "Group stage", "1W–2L"], ["Gad Tsobari", "Wrestling", "Freestyle — Light Flyweight <48 kg", "Group stage", "0W–2L"], ["Yehuda Weissenstein", "Fencing", "Men's foil", "Second round", "W2–L8 (1R 2-3, 2R 0-5)"], ["Esther Shahamorov", "Athletics", "Women's 100 m", "Semifinal (5th)", "11.49"], ["Dan Alon", "Fencing", "Men's foil", "Second round", "W5–L5 (1R 3-2, 2R 2-3)"], ["Esther Shahamorov", "Athletics", "Women's 100 m hurdles", "Semifinal", "Did not start (left Munich before the semifinal)"], ["Shlomit Nir", "Swimming", "Women's 200 m breaststroke", "Heats (6th)", "2:53.60"], ["Shlomit Nir", "Swimming", "Women's 100 m breaststroke", "Heats (8th)", "1:20.90"], ["Yair Michaeli", "Sailing", "Flying Dutchman", "23", "28-22-22-19-25-19-DNS = 171 pts\\n(left Kiel before 7th race)"], ["Shaul Ladani", "Athletics", "Men's 50 km walk", "19", "4:24:38.6\\n(also entered for 20 km walk, but did not start)"], ["Itzhak Nir", "Sailing", "Flying Dutchman", "23", "28-22-22-19-25-19-DNS = 171 pts\\n(left Kiel before 7th race)"], ["Mark Slavin", "Wrestling", "Greco-Roman — Middleweight <82 kg", "—", "(taken hostage before his scheduled event)"], ["Zelig Stroch", "Shooting", "50 metre rifle prone", "57", "589/600"], ["Henry Hershkowitz", "Shooting", "50 metre rifle prone", "23", "593/600"], ["Henry Hershkowitz", "Shooting", "50 metre rifle three positions", "46", "1114/1200"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many players participated in the weightlifting sport? | 3 | 128 | Answer: |
Table InputTable: [["School", "Season", "Record", "Conference Record", "Place", "Postseason"], ["Illinois", "1914–15", "16–0", "12–0", "T1st", "National Champions"], ["Illinois", "1919–20", "9–4", "8–4", "3rd", ""], ["Illinois", "1916–17", "13–3", "10–2", "T1st", "Big Ten Champions"], ["Illinois", "1912–13", "10–6", "7–6", "5th", ""], ["Illinois", "1913–14", "9–4", "7–3", "3rd", ""], ["Illinois", "1917–18", "9–6", "6–6", "T4th", ""], ["Illinois", "1918–19", "6–8", "5–7", "5th", ""], ["Illinois", "1915–16", "13–3", "9–3", "T2nd", ""], ["Illinois", "1912–20", "85–34", "64–31", "–", ""]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many seasons did this coach lead the team to postseason championships? | 2 | 128 | Answer: |
Table InputTable: [["Week", "Date", "TV Time", "Opponent", "Result", "Attendance"], ["16", "December 20, 1998", "FOX 2:05 pm MT", "New Orleans Saints", "W 19–17", "51,617"], ["13", "November 29, 1998", "FOX 11:00 am MT", "at Kansas City Chiefs", "L 34–24", "69,613"], ["3", "September 20, 1998", "ESPN 5:15 pm MT", "Philadelphia Eagles", "W 17–3", "39,782"], ["6", "October 11, 1998", "FOX 1:05 pm MT", "Chicago Bears", "W 20–7", "50,495"], ["14", "December 6, 1998", "FOX 2:15 pm MT", "New York Giants", "L 23–19", "46,128"], ["15", "December 13, 1998", "FOX 11:00 am MT", "at Philadelphia Eagles", "W 20–17", "62,176"], ["2", "September 13, 1998", "FOX 1:15 pm MT", "at Seattle Seahawks", "L 33–14", "57,678"], ["17", "December 27, 1998", "CBS 2:05 pm MT", "San Diego Chargers", "W 16–13", "71,670"], ["12", "November 22, 1998", "FOX 11:00 am MT", "at Washington Redskins", "W 45–42", "63,435"], ["1", "September 6, 1998", "FOX 1:05 pm MT", "at Dallas Cowboys", "L 38–10", "63,602"], ["5", "October 4, 1998", "CBS 1:05 pm MT", "Oakland Raiders", "L 23–20", "53,240"], ["11", "November 15, 1998", "FOX 2:15 pm MT", "Dallas Cowboys", "L 35–28", "71,670"], ["4", "September 27, 1998", "FOX 10:00 am MT", "at St. Louis Rams", "W 20–17", "55,832"], ["9", "November 1, 1998", "FOX 11:00 am MT", "at Detroit Lions", "W 17–15", "66,087"], ["10", "November 8, 1998", "FOX 2:15 pm MT", "Washington Redskins", "W 29–27", "45,950"], ["7", "October 18, 1998", "FOX 10:00 am MT", "at New York Giants", "L 34–7", "70,456"], ["8", "Bye", "Bye", "Bye", "Bye", ""]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:the game on which date had the least attendance? | September 20, 1998 | 128 | Answer: |
Table InputTable: [["Season", "Date", "Location", "Discipline", "Place"], ["2014", "1 Dec 2013", "Beaver Creek, USA", "Giant slalom", "3rd"], ["2014", "22 Dec 2013", "Val-d'Isère, France", "Giant slalom", "1st"], ["2014", "25 Jan 2014", "Cortina d'Ampezzo, Italy", "Downhill", "3rd"], ["2014", "24 Jan 2014", "Cortina d'Ampezzo, Italy", "Downhill", "2nd"], ["2014", "26 Jan 2014", "Cortina d'Ampezzo, Italy", "Super-G", "2nd"], ["2014", "14 Dec 2013", "St. Moritz, Switzerland", "Super-G", "1st"], ["2014", "8 Dec 2013", "Lake Louise, Canada", "Super-G", "2nd"], ["2014", "7 Dec 2013", "Lake Louise, Canada", "Downhill", "2nd"], ["2014", "29 Nov 2013", "Beaver Creek, USA", "Downhill", "2nd"], ["2012", "28 Jan 2012", "St. Moritz, Switzerland", "Downhill", "3rd"], ["2013", "30 Nov 2012", "Lake Louise, Canada", "Downhill", "3rd"], ["2012", "26 Feb 2012", "Bansko, Bulgaria", "Super-G", "2nd"], ["2012", "2 Dec 2011", "Lake Louise, Canada", "Downhill", "2nd"], ["2012", "5 Feb 2012", "Garmisch, Germany", "Super-G", "3rd"], ["2012", "4 Feb 2012", "Garmisch, Germany", "Downhill", "3rd"], ["2013", "1 Mar 2013", "Garmisch, Germany", "Super-G", "1st"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:in how many disciplines does tina weirather compete? | 3 | 128 | Answer: |
Table InputTable: [["Name", "League", "FA Cup", "League Cup", "JP Trophy", "Total"], ["Danny Coles", "3", "0", "0", "0", "3"], ["Liam Sercombe", "1", "0", "0", "0", "1"], ["Scot Bennett", "5", "0", "0", "0", "5"], ["Jamie Cureton", "20", "0", "0", "0", "20"], ["OWN GOALS", "0", "0", "0", "0", "0"], ["Arron Davies", "3", "0", "0", "0", "3"], ["Alan Gow", "4", "0", "0", "0", "4"], ["Guillem Bauza", "2", "0", "0", "0", "2"], ["Pat Baldwin", "1", "0", "0", "0", "1"], ["Jimmy Keohane", "3", "0", "0", "0", "3"], ["John O'Flynn", "11", "0", "1", "0", "12"], ["Jake Gosling", "1", "0", "0", "0", "1"], ["Total", "0", "0", "0", "0", "0"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:no other player but who scored in the league cup? | John O'Flynn | 128 | Answer: |
Table InputTable: [["Rank", "Player", "Nation", "Club", "Goals"], ["2", "Matthew Delicâte", "ENG", "Richmond Kickers", "10"], ["9", "Chris Banks", "USA", "Wilmington Hammerheads", "7"], ["1", "Jhonny Arteaga", "COL", "FC New York", "13"], ["9", "Sallieu Bundu", "SLE", "Charlotte Eagles", "7"], ["9", "George Davis IV", "USA", "Dayton Dutch Lions", "7"], ["3", "José Angulo", "USA", "Harrisburg City Islanders", "9"], ["9", "Sainey Touray", "GAM", "Harrisburg City Islanders", "7"], ["3", "Luke Mulholland", "ENG", "Wilmington Hammerheads", "9"], ["6", "Andriy Budnyy", "UKR", "Wilmington Hammerheads", "8"], ["3", "Maxwell Griffin", "USA", "Orlando City", "9"], ["6", "Andrew Welker", "USA", "Harrisburg City Islanders", "8"], ["6", "Jamie Watson", "USA", "Orlando City", "8"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many players scored at least 9 goals? | 5 | 128 | Answer: |
Table InputTable: [["Place", "Code", "Area (km2)", "Population", "Most spoken language"], ["Remainder of the municipality", "91106", "2,944.04", "10,463", "Northern Sotho"], ["Moletji", "91107", "11.66", "4,989", "Northern Sotho"], ["Dendron", "91103", "2.98", "1,885", "Northern Sotho"], ["Ga-Ramokgopha", "91104", "11.22", "15,806", "Northern Sotho"], ["Bochum", "91102", "11.64", "4,142", "Northern Sotho"], ["Manthata", "91105", "12.24", "22,121", "Northern Sotho"], ["Sekhokho", "91109", "1.24", "1,852", "Northern Sotho"], ["Sekgosese", "91108", "349.99", "46,749", "Northern Sotho"], ["Soekmekaar", "91110", "1.06", "217", "Northern Sotho"], ["Backer", "91101", "0.34", "1,217", "Northern Sotho"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:which place has the smallest number of population? | Soekmekaar | 128 | Answer: |
Table InputTable: [["Region", "Physician (GP & specialist)", "Physician : Population Ratio", "Health Officer", "HO : Population Ratio", "All Nurses", "Nurse : Population Ratio", "Mid-wives", "Mid Wife: Population Ratio", "HEW*", "HEW : Population Ratio"], ["SNNPR", "242", "1:65,817", "220", "1:72,398", "3,980", "1:4,002", "316", "1:50,404", "7,915", "1:2,012"], ["Somalia", "71", "1:65,817", "12", "1:389,415", "314", "1:14,882", "45", "1:103,844", "1,427", "1:3,275"], ["Addis Ababa", "934", "1:3,056", "170", "1:16,791", "3,377", "1:845", "244", "1:11,699", "NA", "-"], ["Amhara", "304", "1:58,567", "434", "1:41,024", "3,790", "1:4,698", "212", "1:83,983", "7,471", "1:2,383"], ["Diredawa", "53", "1:6,796", "19", "1:18,957", "272", "1:1,324", "20", "1:18,009", "142", "1:2,537"], ["Gambella", "13", "1:25,585", "13", "1:25,585", "91", "1:3,655", "4", "1:83,150", "457", "1:728"], ["Oromia", "378", "1:76,075", "448", "1:64,189", "5,040", "1:5,706", "287", "1:100,197", "13856", "1:2,075"], ["Tigray", "101", "1:44,880", "188", "1:24,111", "2,332", "1:1,944", "185", "1:24,502", "1,433", "1:3,163"], ["Ben-Gumuz", "12", "1:59,309", "42", "1:16,945", "452", "1:1,575", "37", "1:19,235", "499", "1:1,426"], ["Harari", "29", "1:6,655", "31", "1:6,226", "276", "1:699", "29", "1:6,655", "47", "1:4,106"], ["Afar", "15", "1:98,258", "29", "1:50,823", "185", "1:7,967", "−", "−", "572", "1:2,577"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:which region has the least number of health officers? | Somalia | 128 | Answer: |
Table InputTable: [["Series Number", "Season Number", "Episode Title", "Premiere Date", "Production Code"], ["7", "7", "Front Page", "January 17, 1999", "107"], ["13", "13", "Hot Dog", "March 14, 1999", "113"], ["1", "1", "Going Up!", "October 25, 1998", "101"], ["11", "11", "JB's Big Break", "February 21, 1999", "111"], ["12", "12", "Bottom's Up", "March 7, 1999", "112"], ["8", "8", "Special FX-Ation", "January 24, 1999", "108"], ["2", "2", "Who's The Man", "November 1, 1998", "102"], ["4", "4", "Close Encounters", "November 15, 1998", "104"], ["10", "10", "Kiss And Tell", "February 7, 1999", "110"], ["9", "9", "The Famous Stone Gold", "January 31, 1999", "109"], ["5", "5", "Hurricane Jules", "November 22, 1998", "105"], ["6", "6", "Switcheroo", "November 29, 1998", "106"], ["3", "3", "Vootle-Muck-A-Heev", "November 8, 1998", "103"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what episode came next after "front page"? | Special FX-Ation | 128 | Answer: |
Table InputTable: [["District", "Incumbent", "2008 Status", "Democratic", "Republican"], ["6", "Tom Tancredo", "Open", "Hank Eng", "Mike Coffman"], ["2", "Mark Udall", "Open", "Jared Polis", "Scott Starin"], ["3", "John Salazar", "Re-election", "John Salazar", "Wayne Wolf"], ["7", "Ed Perlmutter", "Re-election", "Ed Perlmutter", "John W. Lerew"], ["5", "Doug Lamborn", "Re-election", "Hal Bidlack", "Doug Lamborn"], ["1", "Diana DeGette", "Re-election", "Diana DeGette", "George Lilly"], ["4", "Marilyn Musgrave", "Re-election", "Betsy Markey", "Marilyn Musgrave"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what is the 2008 status of tom tancredo? | Open | 128 | Answer: |
Table InputTable: [["Season", "Date", "Location", "Discipline", "Place"], ["1994", "13 Mar 1994", "Whistler, BC, Canada", "Super G", "1st"], ["1993", "27 Feb 1993", "Whistler, BC, Canada", "Downhill", "2nd"], ["1994", "16 Mar 1994", "Vail, CO, USA", "Downhill", "3rd"], ["1995", "11 Dec 1994", "Tignes, France", "Super G", "2nd"], ["1994", "12 Mar 1994", "Whistler, BC, Canada", "Downhill", "3rd"], ["1994", "29 Dec 1993", "Bormio, Italy", "Downhill", "3rd"], ["1994", "12 Dec 1993", "Val-d'Isère, France", "Super G", "3rd"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what is the first location listed on this chart? | Whistler, BC, Canada | 128 | Answer: |
Table InputTable: [["Nr.", "Name", "Area (km²)", "Population (2006)", "Capital", "Club(s)"], ["3", "Cairo", "3,435", "7,786,640", "Cairo", "Al-Ahly - Al Mokawloon - ENPPI - El-Jaish - El-Shorta - Itesalat"], ["1", "Alexandria", "2,900", "4,110,015", "Alexandria", "Al Itthad Al Sakandary - El-Olympi - Haras El Hodood"], ["5", "Giza", "85,153", "6,272,571", "Giza", "Zamalek- Tersana"], ["6", "Ismailia", "1,442", "942,832", "Ismailia", "Ismaily"], ["6", "Suez", "17,840", "510,935", "Suez", "Petrojet"], ["4", "Gharbia", "25,400", "3,790,670", "Tanta", "Ghazl El-Mehalla"], ["2", "Asyut", "25,926", "3,441,597", "Asyut", "Petrol Asyout"], ["7", "Port Said", "72", "570,768", "Port Said", "Al Masry"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many clubs in cairo | 6 | 128 | Answer: |
Table InputTable: [["Name", "Location", "Date Formed", "Area", "Description"], ["Cape Hatteras", "North Carolina\\n35°18′N 75°31′W / 35.30°N 75.51°W", "January 12, 1953", "30,350.65 acres (122.8 km2)", "Located in the Outer Banks, Cape Hatteras is known for its Bodie Island and Cape Hatteras Lighthouses. Popular recreation activities include windsurfing, birdwatching, fishing, shell collecting, and kayaking. Constantly changing from ocean activity, this barrier island provides refuge for the endangered piping plover, seabeach amaranth, and sea turtles."], ["Cape Lookout", "North Carolina\\n34°37′N 76°32′W / 34.61°N 76.54°W", "March 10, 1966", "28,243.36 acres (114.3 km2)", "Cape Lookout National Seashore is made up of three islands of the Outer Banks. It is known for its wild horses and the Cape Lookout Lighthouse. Hiking, camping, fishing, and birdwatching are popular recreational activities. It is also home to two historic villages."], ["Cape Cod", "Massachusetts\\n41°57′N 70°00′W / 41.95°N 70.00°W", "August 7, 1961", "43,608.48 acres (176.5 km2)", "Beyond its nearly 40 miles of beaches, this historic area has Marconi Station, the Three Sisters Lighthouses, and the former North Truro Air Force Station. Cranberry bogs, marshes, and hiking trails provide a look into the flora and fauna of Cape Cod."], ["Cumberland Island", "Georgia\\n30°50′N 81°27′W / 30.83°N 81.45°W", "October 23, 1972", "36,415.13 acres (147.4 km2)", "Cumberland Island is the site of the Plum Orchard estate, Thomas Carnegie's ruined Dungeness mansion, and an African Baptist church. The museum on the mainland preserves Timucua Indian history, Nathaniel Green and Eli Whitney's works, and War of 1812 battles."], ["Point Reyes", "California\\n38°00′N 123°00′W / 38.00°N 123.00°W", "October 20, 1972", "71,067.78 acres (287.6 km2)", "Historic locations on Point Reyes Peninsula include the Point Reyes Lighthouse and Lifeboat Station and a recreated Coast Miwok village. Gray whales can be seen as they migrate near the seashore, and tule elk and elephant seals populate the wilderness area."], ["Canaveral", "Florida\\n28°46′N 80°47′W / 28.77°N 80.78°W", "January 3, 1975", "57,661.69 acres (233.3 km2)", "Adjacent to the Kennedy Space Center, this barrier island has a variety of recreational activities including hiking, boating, and fishing. The Seminole Rest features an ancient Native American mound, and Eldora Statehouse shows historic life on the lagoon. Florida's longest undeveloped Atlantic beach surrounds Mosquito Lagoon, which is home to dolphins, manatees, and sea turtles, along with a variety of sea grasses."], ["Fire Island", "New York\\n40°42′N 72°59′W / 40.70°N 72.98°W", "September 11, 1964", "19,579.47 acres (79.2 km2)", "Fire Island, a barrier island south of Long Island, has the historic William Floyd House and Fire Island Lighthouse. The beaches and dunes are complemented by a sunken forest, wetlands, and seventeen communities."], ["Assateague Island", "Maryland, Virginia\\n38°05′N 75°13′W / 38.08°N 75.21°W", "September 21, 1965", "39,726.75 acres (160.8 km2)", "As a barrier island, Assateague Island is continually shaped by wind and waves. It is known for its feral horses and is also home to deer, crabs, fox, and migrating snow geese. Main vegetation includes American beach grass, saltmarsh cordgrass and sea rocket."], ["Padre Island", "Texas\\n27°00′N 97°23′W / 27°N 97.38°W", "April 6, 1968", "130,434.27 acres (527.8 km2)", "Padre Island, the world's longest undeveloped barrier island, is a nesting ground for the Kemp's ridley sea turtle and a migratory site for Least Terns, Brown Pelicans, and Piping Plovers. Malaquite Beach provides a variety of recreational activities, and Novillo Line Camp has the remains of a cattle ranch. The military used part of the island as a bombing range during WWII."]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:which location is a cape and is home to a single lighthouse? | Cape Lookout | 128 | Answer: |
Table InputTable: [["Date", "Opponent", "Score", "Result", "Location"], ["March 7, 1964", "Michigan State", "13–4", "Win", "Coliseum, Ann Arbor, MI"], ["March 6, 1964", "Michigan State", "9–4", "Win", "East Lansing, MI"], ["March 12, 1964", "Michigan Tech", "4–3", "Win", "Coliseum, Ann Arbor, MI"], ["Jan. 24, 1964", "Michigan Tech", "6–2", "Win", "Coliseum, Ann Arbor, MI"], ["Feb. 15, 1964", "Michigan State", "7–2", "Win", "Coliseum, Ann Arbor, MI"], ["Jan. 25, 1964", "Michigan Tech", "5–3", "Win", "Coliseum, Ann Arbor, MI"], ["Feb. 22, 1964", "Minnesota", "8–2", "Win", "Coliseum, Ann Arbor, MI"], ["Jan. 10, 1964", "Minnesota", "5–1", "Win", "Minneapolis, MN"], ["Dec. 14, 1963", "Toronto", "10–0", "Win", "Coliseum, Ann Arbor, MI"], ["Feb. 29, 1964", "Michigan Tech", "4–3", "Win", "Houghton, MI"], ["Jan. 31, 1964", "Colorado College", "7–0", "Win", "Coliseum, Ann Arbor, MI"], ["Feb. 21, 1964", "Minnesota", "6–3", "Win", "Coliseum, Ann Arbor, MI"], ["Feb. 14, 1964", "Michigan State", "2–0", "Win", "East Lansing, MI"], ["Jan. 17, 1964", "Loyola (Montreal)", "12–1", "Win", "Coliseum, Ann Arbor, MI"], ["March 13, 1964", "Michigan Tech", "5–5", "Tie", "Coliseum, Ann Arbor, MI"], ["Nov. 29, 1963", "Queen's", "9–5", "Win", "Coliseum, Ann Arbor, MI"], ["Jan. 18, 1964", "Loyola (Montreal)", "14–2", "Win", "Coliseum, Ann Arbor, MI"], ["Feb. 1, 1964", "Colorado College", "12–4", "Win", "Coliseum, Ann Arbor, MI"], ["Nov. 30, 1963", "Queen's", "9–5", "Win", "Coliseum, Ann Arbor, MI"], ["Feb. 8, 1964", "Ohio State", "21–0", "Win", "Columbus, OH"], ["Feb. 7, 1964", "Ohio", "14–0", "Win", "Athens, OH"], ["March 14, 1964", "Denver", "2–6", "Loss", "Coliseum, Ann Arbor, MI"], ["Jan. 8, 1964", "Minn-Duluth", "7–2", "Win", "Duluth, MN"], ["Feb. 28, 1964", "Michigan Tech", "1–3", "Loss", "Houghton, MI"], ["Jan. 7, 1964", "Minn-Duluth", "8–4", "Win", "Duluth, MN"], ["Dec. 13, 1963", "Toronto", "3–5", "Loss", "Coliseum, Ann Arbor, MI"], ["March 21, 1964", "Denver", "6–3", "Win", "Denver, CO"], ["March 20, 1964", "Providence", "3–2", "Win", "Denver, CO"], ["Jan. 11, 1964", "Minnesota", "5–6", "Loss", "Minneapolis, MN"], ["", "", "217–80", "24–4–1", ""]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what was the total number of wins for the wolverines that season? | 24 | 128 | Answer: |
Table InputTable: [["Year", "Result", "Award", "Film"], ["2007", "Nominated", "People's Choice Award Favorite Male TV Star", ""], ["2008", "Nominated", "People's Choice Award Favorite Male TV Star", ""], ["2008", "Nominated", "Outstanding Lead Actor - Comedy Series", "Two and a Half Men"], ["2002", "Nominated", "Kids' Choice Awards Favorite Television Actor", "Two and a Half Men"], ["2007", "Nominated", "Teen Choice Award Choice TV Actor: Comedy", "Two and a Half Men"], ["2012", "Won", "WWE Slammy Award Top Social Media Ambassador", "WWE Raw"], ["2001", "Nominated", "ALMA Award Outstanding Actor in a Television Series", "Spin City"], ["2006", "Nominated", "Golden Globe Award for Best Actor – Television Series Musical or Comedy", "Two and a Half Men"], ["2002", "Nominated", "ALMA Award Outstanding Actor in a Television Series", "Spin City"], ["2007", "Nominated", "Emmy Award for Outstanding Lead Actor - Comedy Series", "Two and a Half Men"], ["2008", "Nominated", "Teen Choice Awards Choice TV Actor: Comedy", "Two and a Half Men"], ["2006", "Nominated", "Emmy Award for Outstanding Lead Actor - Comedy Series", "Two and a Half Men"], ["2010", "Nominated", "SAG Award Outstanding Performance by a Male Actor in a Comedy Series", "Two and a Half Men"], ["2005", "Nominated", "Golden Globe Award for Best Actor – Television Series Musical or Comedy", "Two and a Half Men"], ["2005", "Nominated", "SAG Award Outstanding Performance by a Male Actor in a Comedy Series", "Two and a Half Men"], ["1999", "Nominated", "SAG Award Outstanding Performance by a Cast in a Theatrical Motion Picture", "Being John Malkovich"], ["2006", "Won", "Golden Icon Award Best Actor - Comedy Series", "Two and a Half Men"], ["1999", "Nominated", "Online Film Critics Society Award for Best Cast", "Being John Malkovich"], ["1989", "Won", "Bronze Wrangler Theatrical Motion Picture", "Young Guns"], ["2002", "Won", "Golden Globe Award Best Performance by an Actor in a Television Series - Musical or Comedy", "Spin City"], ["2008", "Won", "ALMA Award Outstanding Actor in a Comedy Television Series", "Two and a Half Men"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many times was a nomination awarded? | 5 | 128 | Answer: |
Table InputTable: [["Date", "Opponent", "Site", "Result"], ["November 6", "Kentucky", "Rickwood Field • Birmingham, AL", "W 14–0"], ["November 13", "Florida", "Cramton Bowl • Montgomery, AL", "W 49–0"], ["January 1, 1927", "vs. Stanford*", "Rose Bowl • Pasadena, CA (Rose Bowl)", "T 7–7"], ["October 30", "LSU", "Denny Field • Tuscaloosa, AL (Rivalry)", "W 24–0"], ["October 16", "at Georgia Tech", "Grant Field • Atlanta, GA", "W 21–0"], ["October 2", "at Vanderbilt", "Dudley Field • Nashville, TN", "W 19–7"], ["September 24", "Millsaps*", "Denny Field • Tuscaloosa, AL", "W 54–0"], ["October 9", "at Mississippi A&M", "Meridian Fairgrounds • Meridian, MS (Rivalry)", "W 26–7"], ["November 25", "Georgia", "Rickwood Field • Birmingham, AL", "W 33–6"], ["October 23", "Sewanee", "Rickwood Field • Birmingham, AL", "W 2–0"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:was the score at kentucky higher than florida? | No | 128 | Answer: |
Table InputTable: [["Position", "Sail Number", "Yacht", "State/Country", "Yacht Type", "LOA\\n(Metres)", "Skipper", "Elapsed Time\\nd:hh:mm:ss"], ["4", "AUS70", "Ragamuffin", "NSW", "Farr 50", "15.15", "Syd Fischer", "3:06:11:29"], ["3", "YC1000", "Ausmaid", "SA", "Farr 47", "14.24", "Kevan Pearce", "3:06:02:29"], ["7", "6606", "Quest", "NSW", "Nelson Marek 46", "14.12", "Bob Steel", "3:14:41:28"], ["2", "C1", "Brindabella", "NSW", "Jutson 79", "24.07", "George Snow", "2:21:55:06"], ["8", "9090", "Industrial Quest", "QLD", "Nelson Marek 43", "13.11", "Kevin Miller", "3:14:58:46"], ["10", "8338", "AFR Midnight Rambler", "NSW", "Hick 35", "10.66", "Ed Psaltis\\nBob Thomas", "3:16:04:40"], ["6", "SM1", "Fudge", "VIC", "Elliot 56", "17.07", "Peter Hansen", "3:11:00:26"], ["9", "4826", "Aspect Computing", "NSW", "Radford 16.5 Sloop", "16.50", "David Pescud", "3:15:28:24"], ["1", "US17", "Sayonara", "USA", "Farr ILC Maxi", "24.13", "Larry Ellison", "2:19:03:32"], ["5", "COK1", "Nokia", "CI", "Farr Ketch Maxi", "25.20", "David Witt", "3:09:19:00"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what are the names of the top five yachts, arranged in alphabetical order? | Ausmaid, Brindabella, Nokia, Ragamuffin, Sayonara | 128 | Answer: |
Table InputTable: [["Specifications", "Foundation", "Essentials", "Standard", "Datacenter"], ["Remote Desktop Services limits", "50 Remote Desktop Services connections", "Gateway only", "Unlimited", "Unlimited"], ["Virtualization rights", "N/A", "Either in 1 VM or 1 physical server, but not both at once", "2 VMs", "Unlimited"], ["File Services limits", "1 standalone DFS root", "1 standalone DFS root", "Unlimited", "Unlimited"], ["Licensing model", "Per server", "Per server", "Per CPU pair + CAL", "Per CPU pair + CAL"], ["Windows Server Update Services", "No", "Yes", "Yes", "Yes"], ["Windows Deployment Services", "Yes", "Yes", "Yes", "Yes"], ["Active Directory Rights Management Services", "Yes", "Yes", "Yes", "Yes"], ["Server Core mode", "No", "No", "Yes", "Yes"], ["Network Policy and Access Services limits", "50 RRAS connections and 10 IAS connections", "250 RRAS connections, 50 IAS connections, and 2 IAS Server Groups", "Unlimited", "Unlimited"], ["Active Directory Certificate Services", "Certificate Authorities only", "Certificate Authorities only", "Yes", "Yes"], ["Application server role", "Yes", "Yes", "Yes", "Yes"], ["User limit", "15", "25", "Unlimited", "Unlimited"], ["DNS server role", "Yes", "Yes", "Yes", "Yes"], ["Active Directory Federation Services", "Yes", "No", "Yes", "Yes"], ["Distribution", "OEM only", "Retail, volume licensing, OEM", "Retail, volume licensing, OEM", "Volume licensing and OEM"], ["Fax server role", "Yes", "Yes", "Yes", "Yes"], ["Print and Document Services", "Yes", "Yes", "Yes", "Yes"], ["Active Directory Domain Services", "Must be root of forest and domain", "Must be root of forest and domain", "Yes", "Yes"], ["Server Manager", "Yes", "Yes", "Yes", "Yes"], ["Memory limit", "32 GB", "64 GB", "4 TB", "4 TB"], ["Web Services (Internet Information Services)", "Yes", "Yes", "Yes", "Yes"], ["Active Directory Lightweight Directory Services", "Yes", "Yes", "Yes", "Yes"], ["Processor chip limit", "1", "2", "64", "64"], ["DHCP role", "Yes", "Yes", "Yes", "Yes"], ["UDDI Services", "Yes", "Yes", "Yes", "Yes"], ["Hyper-V", "No", "No", "Yes", "Yes"], ["Windows Powershell", "Yes", "Yes", "Yes", "Yes"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:which edition has unlimited remote desktop services and virtulization rights? | Datacenter | 128 | Answer: |
Table InputTable: [["Band", "Disc/Song", "Released", "Disc Description", "Disk Size", "Image"], ["Guns N' Roses", "Paradise City", "1989", "Shape of a Colt \"Peacemaker\"", "7\"", ""], ["The Coconuts (Side project of Kid Creole and the Coconuts)", "Did You Have To Love Me Like You Did", "1983", "In the shape of a coconut.", "7\"", ""], ["Guns N' Roses", "Sweet Child o' Mine", "1988", "Shape of the classic logo of the cross and skulls of the five band members", "7\"", ""], ["Devo", "Beautiful World b/w Nu-Tra", "1981", "Shaped like an astronaut head", "", ""], ["OMD", "La Femme Accident", "1985", "", "", ""], ["The Fat Boys", "Wipe Out", "", "Shaped like a Hamburger", "7\"", ""], ["Gary Numan", "Berserker", "1984", "Shaped like Numan's head.", "7\"", ""], ["Red Box", "Lean On Me b/w Stinging Bee", "1985", "Hexagonal red vinyl. Looks like a red box in 2D; flipside is a band photo.", "7\"", ""], ["The Enemy", "You're not alone", "2007", "Square shaped. Has the single cover art on the A-side and a black and white picture of the band on the B-side with track listing.", "7\"", ""], ["Gary Numan", "Warriors", "1983", "Shaped like a Jet Fighter.", "7\"", ""], ["Yeah Yeah Yeahs", "Cheated Hearts", "2006", "Heart shaped.", "7\"", ""], ["Barnes & Barnes", "Fish Heads: Barnes & Barnes' Greatest Hits", "1982", "Shaped as a fish head", "12\"", ""], ["Kiss", "Lick It Up", "1983", "Shaped like an armored tank", "", ""], ["Monster Magnet", "Dopes to Infinity", "1995", "Shaped like the lead singer Dave Wyndorf's head.", "12\"", ""], ["Killing Joke", "Loose Cannon", "2003", "shaped yellow evil clown head image from the eponymous 2003 album sleeve", "", ""], ["Men Without Hats", "I Got the Message", "1983", "", "", ""], ["Guns N' Roses", "Nightrain", "1989", "Shape of a suitcase", "7\"", ""], ["The Mars Volta", "Mr. Muggs", "2008", "In the shape of a clear planchette.", "7\"", ""], ["Joe Strummer", "Love Kills", "", "Shaped like a gun", "7\"", "A gun"], ["Less Than Jake", "Cheese", "1998", "Shaped like a piece of swiss cheese. 1000 pressed in yellow. 500 pressed in green (\"Moldy Version\").", "7\"", ""], ["U2", "The Unforgettable Fire (single)", "1985", "Shaped as letter & number \"U2\" with various pictures of the band from the period.", "7\"", "U2"], ["Monster Magnet", "Negasonic Teenage Warhead", "", "Shaped like a mushroom cloud", "12\"", ""], ["Saxon", "Back on the Streets Again", "", "Shaped as an apple (as is printed on one side of the disk).", "7\"", ""], ["Gangrene", "Sawblade EP", "2010", "In the shape of a circular sawblade.", "", ""], ["Tangerine Dream", "Warsaw in the Sun", "1984", "The record is in the shape of Poland and has several images including Lech Wałęsa and Pope John Paul II.", "7\"", ""], ["Broken English", "Comin on Strong", "1987", "Shaped as the 3 band members wearing Ghostbusters outfits holding guitars.", "", ""], ["Men Without Hats", "The Safety Dance", "1982", "Oddly shaped picture disc of a man and a woman dancing", "", ""]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what is the difference in years between paradise city and sweet child o' mine? | 1 year | 128 | Answer: |
Table InputTable: [["Date", "Opponent", "Score", "Result", "Location"], ["", "", "217–80", "24–4–1", ""], ["Jan. 10, 1964", "Minnesota", "5–1", "Win", "Minneapolis, MN"], ["March 21, 1964", "Denver", "6–3", "Win", "Denver, CO"], ["March 20, 1964", "Providence", "3–2", "Win", "Denver, CO"], ["Feb. 7, 1964", "Ohio", "14–0", "Win", "Athens, OH"], ["March 6, 1964", "Michigan State", "9–4", "Win", "East Lansing, MI"], ["Jan. 17, 1964", "Loyola (Montreal)", "12–1", "Win", "Coliseum, Ann Arbor, MI"], ["Feb. 8, 1964", "Ohio State", "21–0", "Win", "Columbus, OH"], ["Jan. 11, 1964", "Minnesota", "5–6", "Loss", "Minneapolis, MN"], ["Jan. 31, 1964", "Colorado College", "7–0", "Win", "Coliseum, Ann Arbor, MI"], ["Jan. 18, 1964", "Loyola (Montreal)", "14–2", "Win", "Coliseum, Ann Arbor, MI"], ["March 7, 1964", "Michigan State", "13–4", "Win", "Coliseum, Ann Arbor, MI"], ["Feb. 22, 1964", "Minnesota", "8–2", "Win", "Coliseum, Ann Arbor, MI"], ["Dec. 14, 1963", "Toronto", "10–0", "Win", "Coliseum, Ann Arbor, MI"], ["Feb. 14, 1964", "Michigan State", "2–0", "Win", "East Lansing, MI"], ["March 14, 1964", "Denver", "2–6", "Loss", "Coliseum, Ann Arbor, MI"], ["March 12, 1964", "Michigan Tech", "4–3", "Win", "Coliseum, Ann Arbor, MI"], ["Feb. 21, 1964", "Minnesota", "6–3", "Win", "Coliseum, Ann Arbor, MI"], ["Jan. 8, 1964", "Minn-Duluth", "7–2", "Win", "Duluth, MN"], ["Jan. 25, 1964", "Michigan Tech", "5–3", "Win", "Coliseum, Ann Arbor, MI"], ["Jan. 24, 1964", "Michigan Tech", "6–2", "Win", "Coliseum, Ann Arbor, MI"], ["Jan. 7, 1964", "Minn-Duluth", "8–4", "Win", "Duluth, MN"], ["Feb. 15, 1964", "Michigan State", "7–2", "Win", "Coliseum, Ann Arbor, MI"], ["March 13, 1964", "Michigan Tech", "5–5", "Tie", "Coliseum, Ann Arbor, MI"], ["Feb. 29, 1964", "Michigan Tech", "4–3", "Win", "Houghton, MI"], ["Dec. 13, 1963", "Toronto", "3–5", "Loss", "Coliseum, Ann Arbor, MI"], ["Feb. 1, 1964", "Colorado College", "12–4", "Win", "Coliseum, Ann Arbor, MI"], ["Nov. 30, 1963", "Queen's", "9–5", "Win", "Coliseum, Ann Arbor, MI"], ["Nov. 29, 1963", "Queen's", "9–5", "Win", "Coliseum, Ann Arbor, MI"], ["Feb. 28, 1964", "Michigan Tech", "1–3", "Loss", "Houghton, MI"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many games were decided by only one goal? | 4 | 128 | Answer: |
Table InputTable: [["Episode", "Show #", "Iron Chef", "Challenger", "Challenger specialty", "Secret ingredient(s) or theme", "Winner", "Final score"], ["6", "IA0506", "Bobby Flay", "Kurt Boucher", "French-American", "Arctic char", "Bobby Flay", "46-39"], ["5", "IA0504", "Cat Cora", "Mark Tarbell", "Seasonal Organic", "Apples", "Mark Tarbell", "50-44"], ["2", "IA0508", "Mario Batali", "Tony Liu", "Pan-European", "Opah", "Mario Batali", "55-47"], ["7", "IA0510", "Mario Batali", "Charles Clark", "New American", "Halibut", "Mario Batali", "51-50"], ["1", "IA0502", "Bobby Flay", "Ben Ford", "Regional American", "Blue foot chicken", "Bobby Flay", "44-35"], ["4", "IA0501", "Mario Batali", "Andrew Carmellini", "Urban Italian", "Parmigiano-Reggiano", "Mario Batali", "56-55"], ["9", "IASP07", "Michael Symon", "Ricky Moore", "Contemporary American", "Traditional Thanksgiving", "Michael Symon", "51-43"], ["11", "IA0503", "Cat Cora", "Todd Richards", "Modern Southern", "Carrots", "Cat Cora", "48-46"], ["10", "IASP08", "Cat Cora & Paula Deen", "Tyler Florence & Robert Irvine", "Southern (Deen), Contemporary American (Florence), International (Irvine)", "Sugar", "Cat Cora & Paula Deen", "49-47"], ["12", "IA0505", "Masaharu Morimoto", "Fortunato Nicotra", "Seasonal Italian", "Kampachi", "Masaharu Morimoto", "59-50"], ["3", "IA0509", "Cat Cora", "Alexandra Guarnaschelli", "French-American", "Farmers' Market", "Cat Cora", "45-41"], ["8", "IA0507", "Cat Cora", "Mary Dumont", "French-American", "Milk and cream", "Cat Cora", "51-46"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:mark tarbell beat the iron chef. what was his score? | 50 | 128 | Answer: |
Table InputTable: [["Episode no.", "Airdate", "Viewers", "BBC Three weekly ranking", "Multichannels rank"], ["9", "20 June 2013", "1,204,000", "6", "9"], ["3", "9 May 2013", "885,000", "1", "11"], ["1", "25 April 2013", "979,000", "2", "9"], ["4", "16 May 2013", "880,000", "1", "13"], ["6", "30 May 2013", "1,094,000", "1", "3"], ["10", "27 June 2013", "730,000", "N/A", "28"], ["5", "23 May 2013", "1,092,000", "1", "5"], ["8", "13 June 2013", "840,000", "5", "19"], ["7", "6 June 2013", "975,000", "2", "6"], ["2", "2 May 2013", "978,000", "1", "11"], ["11", "4 July 2013", "N/A", "N/A", "N/A"], ["12", "11 July 2013", "", "", ""]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many channels have a multichannels rank of 9? | 2 | 128 | Answer: |
Table InputTable: [["Num", "Pod Color", "Nickname", "Short Description", "Episode"], ["049", "White", "Picker", "This experiment was seen in pod form in Stitch! The Movie. His pod says 49 instead of 049, possibly due to the angle. Function unknown.", "Stitch! The Movie"], ["074", "White", "Welco", "This experiment was seen in pod form in Stitch! The Movie. Pod says 74 instead of 074.", "Stitch! The Movie"], ["094", "White", "Louis B.", "Seen in pod form in Stitch! The Movie. Function unknown. Pod says 94 instead of 094.", "Stitch! The Movie"], ["082", "White", "Plunge", "Seen in pod form in Stitch! The Movie. Function unknown. Pod says 82 instead of 082.", "Stitch! The Movie"], ["078", "White", "Snozzle", "Seen in pod form in Stitch! The Movie. Pod says 78 instead of 078. Function unknown.", "Leroy & Stitch"], ["071", "Yellow", "Penny", "Seen in pod form in Stitch! The Movie. Pod says 71 instead of 071. Function unknown.", "Leroy & Stitch"], ["070", "White", "Flapjack", "Seen in pod form in Stitch! The Movie. Function unknown. Pod says 70 instead of 070.", "Stitch! The Movie"], ["014", "White", "Kernel", "A tan gourd-shaped experiment with a large opening at the top of his head. Designed to pop popcorn. His one true place is in a movie theater. Was mentioned in \"Angel\" when Jumba said \"624 is harmless early experiment. Designed to...pop popcorn for Jumba's movie night.\"", "Leroy & Stitch"], ["019", "White", "Clumsy", "Seen in pod form in Stitch! The Movie. Pod says 19 instead of 019. Function unknown.", "Leroy & Stitch"], ["036", "", "Poki", "A small yellow and brown opossum-like experiment with a spiked tail. Designed to poke holes in liquid containers. Was seen in \"Shoe.\"", "203, 215, Leroy & Stitch"], ["001", "Blue", "Shrink", "A small purple experiment with a white lower jaw and chest, three wobbly legs, two stubby little arms and two floppy antennae with two rings on each antenna. Designed to zap a green ray from his antennas to change the size of objects. His picture appears on the wall of Jumba's lab in Leroy & Stitch, along with several other pictures of Jumba and Dr. Hämsterviel's early accomplishments.", "Leroy & Stitch"], ["090", "", "Fetchit", "This experiment was activated when Mrs. Hasagawa's cats were. Function unknown.", "220"], ["011", "Green", "Inkstain", "", "Leroy & Stitch"], ["008", "Orange/Brown", "Carmine", "", "Leroy & Stitch"], ["051", "Green", "Hocker", "A green experiment with a huge blue nose and a yellow spot around his eyes and a yellow stripe on his ears and tail (In his episode the spots and stripes were originally red.). Designed to spit acidic saliva that can burn through wood in about three seconds. His one true place is with Mrs. Hasagawa as one of her \"cats.\"", "220, Leroy & Stitch"], ["086", "", "Clink", "A big green mouthless crab-like experiment with four legs, two large claws and a window on its chest. Able to capture and confine any other experiment inside the holding tank in his stomach by splitting in half, surrounding whatever he wants to catch, and joining together again. When Clink splits in two, he works with himself, yet he seems to have a separate mind for each half. 20 years in the future, Lilo, Stitch, and Skip encountered Clink in the possession of Hämsterviel, when Hämsterviel ruled Earth.", "206"], ["002", "Purple", "Doubledip", "A purple opossum-like experiment with two light purple stripes on the back of his ears, beady eyes and an orange nose (In Leroy & Stitch, his nose is dark purple). Designed to double-dip food. His one true place is with Mrs. Hasagawa as one of her \"cats\". He somehow changed in size in Leroy & Stitch.", "220, Leroy & Stitch"], ["052", "", "Coco", "A chocolate-colored pink-haired lizard/Stitch-like experiment. Designed to turn things into chocolate (from a Disney Adventures magazine). She was given to Lilo by Stitch for her birthday.", "Disney Adventures Magazine"], ["054", "Blue", "Fudgy", "An experiment made of chocolate that looks like a blob. Designed to drown people in his sticky sweetness. When he was activated, he was called 119, and he was mistaken for experiment 611. The mistake with his number was due to Jumba's untidy database, although Jumba later corrected this mistake. Was rescued in \"Snafu.\"", "119, 226"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many experiments have a green pod color? | 4 | 128 | Answer: |
Table InputTable: [["Date of Incident", "Offender", "Team", "Offense", "Date of Action", "Length"], ["January 8, 2012", "Jean-Francois Jacques", "Anaheim Ducks", "Illegal hit to the head of R.J. Umberger.", "January 9, 2012", "3 games"], ["May 6, 2012", "Claude Giroux", "Philadelphia Flyers", "Illegal hit to the head of Dainius Zubrus.", "May 7, 2012", "1 game‡ (1 post-season)"], ["September 24, 2011", "Jean-Francois Jacques", "Anaheim Ducks", "Leaving bench to initiate a fight with Mike Duco.", "September 27, 2011", "9 games† (4 pre-season, 5 regular season)"], ["November 26, 2011", "Max Pacioretty", "Montreal Canadiens", "Illegal hit to the head of Kris Letang.", "November 28, 2011", "3 games"], ["November 23, 2011", "Andre Deveaux", "New York Rangers", "Illegal hit to the head of Tomas Fleischmann.", "November 23, 2011", "3 games"], ["December 31, 2011", "Ian Cole", "St. Louis Blues", "Illegal hit to the head of Justin Abdelkader.", "January 1, 2012", "3 games"], ["January 14, 2012", "Dane Byers", "Columbus Blue Jackets", "Illegal hit to the head of Andrew Desjardins.", "January 16, 2012", "3 games"], ["January 3, 2012", "Rene Bourque", "Calgary Flames", "Elbowing Nicklas Backstrom.", "January 4, 2012", "5 games"], ["April 17, 2012", "Raffi Torres", "Phoenix Coyotes", "Late charge to the head of Marian Hossa.", "April 21, 2012", "25 games\\nreduced to 21 games‡ (13 post-season)*"], ["April 15, 2012", "James Neal", "Pittsburgh Penguins", "Charging Claude Giroux.", "April 17, 2012", "1 game‡ (1 post-season)"], ["September 30, 2011", "Clarke MacArthur", "Toronto Maple Leafs", "Illegal hit to the head of Justin Abdelkader.", "October 1, 2011", "3 games† (1 pre-season, 2 regular season)"], ["September 26, 2011", "Tom Sestito", "Philadelphia Flyers", "Checking Andre Deveaux from behind.", "September 28, 2011", "4 games† (2 pre-season, 2 regular season)"], ["February 12, 2012", "Zac Rinaldo", "Philadelphia Flyers", "Charging Jonathan Ericsson.", "February 13, 2012", "2 games"], ["March 20, 2012", "Shane Doan", "Phoenix Coyotes", "Elbowing Jamie Benn.", "March 21, 2012", "3 games"], ["March 8, 2012", "Mike Green", "Washington Capitals", "Illegal hit to the head of Brett Connolly.", "March 9, 2012", "3 games"], ["May 15, 2012", "Martin Hanzal", "Phoenix Coyotes", "Boarding Dustin Brown.", "May 16, 2012", "1 game‡ (1 post-season)"], ["September 28, 2011", "Brendan Smith", "Detroit Red Wings", "Illegal hit to the head of Ben Smith.", "September 30, 2011", "8 games† (3 pre-season, 5 regular season)"], ["October 8, 2011", "Pierre-Marc Bouchard", "Minnesota Wild", "High sticking Matt Calvert.", "October 9, 2011", "2 games"], ["April 15, 2012", "Craig Adams", "Pittsburgh Penguins", "Instigator penalty in the last five minutes of a game.", "April 16, 2012", "1 game‡ (1 post-season)"], ["May 4, 2012", "Rostislav Klesla", "Phoenix Coyotes", "Boarding Matt Halischuk.", "May 6, 2012", "1 game‡ (1 post-season)"], ["September 24, 2011", "Brad Boyes", "Buffalo Sabres", "Illegal hit to the head of Joe Colborne.", "September 25, 2011", "2 games† (2 pre-season)"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many games total did jean-francois jacques play in when he was the offender? | 2 | 128 | Answer: |
Table InputTable: [["Alpha-numeric Code", "Station name\\nEnglish", "Station name\\nChinese", "Station name\\nTamil", "Opening"], ["BP8", "Pending", "秉定", "பெண்டிங்", "6 November 1999"], ["BP5", "Phoenix", "凤凰", "பீனிக்ஸ்", "6 November 1999"], ["BP7", "Petir", "柏提", "பெட்டீர்", "6 November 1999"], ["PTC / NE17", "Punggol", "榜鹅", "பொங்கோல்", "29 January 2005"], ["BP11", "Segar", "实加", "செகார்", "6 November 1999"], ["BP13", "Senja", "信佳", "செஞ்சா", "6 November 1999"], ["PE2", "Meridian", "丽园", "மெரிடியன்", "29 January 2005"], ["SE1", "Compassvale", "康埔桦", "கம்பஸ்வேல்", "18 January 2003"], ["BP12", "Jelapang", "泽拉邦", "ஜேலப்பாங்", "6 November 1999"], ["BP3", "Keat Hong", "吉丰", "கியட் ஹோங்", "6 November 1999"], ["BP10", "Fajar", "法嘉", "பஜார்", "6 November 1999"], ["STC / NE16", "Sengkang", "盛港", "செங்காங்", "18 January 2003"], ["SW5", "Fernvale", "芬微", "பெர்ன்வேல்", "29 January 2005"], ["SE2", "Rumbia", "棕美", "ரூம்பியா", "18 January 2003"], ["BP4", "Teck Whye", "德惠", "டெக் வாய்", "6 November 1999"], ["BP2", "South View", "南山", "சவுத் வியூ", "6 November 1999"], ["PE4", "Riviera", "里维拉", "றிவியாரா", "29 January 2005"], ["SW4", "Thanggam", "丹甘", "தங்கம்", "29 January 2005"], ["BP9", "Bangkit", "万吉", "பங்கிட்", "6 November 1999"], ["PE6", "Oasis", "绿洲", "ஓய்சிஸ்", "15 June 2007"], ["PE3", "Coral Edge", "珊瑚", "கோரல் எட்ஜ்", "29 January 2005"], ["SW8", "Renjong", "仁宗", "ரெஞ்சோங்", "29 January 2005"], ["SW2", "Farmway", "农道", "பாம்வே", "15 November 2007"], ["PE1", "Cove", "海湾", "கோவ்", "29 January 2005"], ["PW7", "Soo Teck", "树德", "ஸூ டெக்", "TBA"], ["BP14", "Ten Mile Junction", "十里广场", "பத்தாம் கல் சந்திப்பு", "6 November 1999"], ["PE5", "Kadaloor", "卡达鲁", "கடலூர்", "29 January 2005"], ["SE4", "Kangkar", "港脚", "கங்கார்", "18 January 2003"], ["SE5", "Ranggung", "兰岗", "ரங்கோங்", "18 January 2003"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:which date has the most lrt stations open? | 6 November 1999 | 128 | Answer: |
Table InputTable: [["Season", "League\\nPos.", "League\\nCompetition", "League\\nTop scorer", "Danish Cup", "Europe", "Others"], ["2009-10", "3", "2009-10 Superliga", "Morten Rasmussen (12)", "4th round", "EC3 qual play-off round", ""], ["2007-08", "8", "2007-08 Superliga", "Morten Rasmussen (7)\\nMartin Ericsson (7)", "Winner", "", ""], ["2008-09", "3", "2008-09 Superliga", "Morten Rasmussen (9)\\nAlexander Farnerud (9)\\nOusman Jallow (9)", "Semi-final", "EC3 1st round", ""], ["2006-07", "6", "2006-07 Superliga", "Morten Rasmussen (15)", "4th round", "EC3 1st round", "Royal League winner\\nDanish League Cup winner"], ["1982-83", "4", "1983 1st Division", "Brian Chrøis (12)", "4th round", "", ""], ["1996-97", "1", "1996-97 Superliga", "Peter Møller (22)", "Semi-final", "EC1 qualification round\\nEC3 quarter-final", "Danish Supercup winner"], ["2001-02", "1", "2001-02 Superliga", "Peter Madsen (22)", "5th round", "EC3 3rd round", ""], ["1983-84", "4", "1984 1st Division", "Jens Kolding (11)", "3rd round", "", ""], ["1995-96", "1", "1995-96 Superliga", "Peter Møller (15)", "Finalist", "EC3 3rd round", ""], ["1985-86", "2", "1986 1st Division", "Claus Nielsen (16)", "Quarter-final", "", ""], ["1984-85", "1", "1985 1st Division", "Claus Nielsen (17)", "3rd round", "", ""], ["2005-06", "2", "2005-06 Superliga", "Johan Elmander (13)", "Semi-final", "EC1 qual 3rd round\\nEC3 group stage", "Royal League group stage\\nDanish League Cup winner"], ["1994-95", "2", "1994-95 Superliga", "Mark Strudal (12)", "Quarter-final", "EC2 2nd round", "Danish Supercup winner"], ["1997-98", "1", "1997-98 Superliga", "Ebbe Sand (28)", "Winner", "EC1 qual 2nd round\\nEC3 1st round", "Danish Supercup winner"], ["1999-00", "2", "1999-00 Superliga", "Bent Christensen (13)", "Semi-final", "EC1 qual 3rd round\\nEC3 1st round", ""], ["1986-87", "1", "1987 1st Division", "Claus Nielsen (20)", "4th round", "EC1 quarter-final", ""], ["2002-03", "2", "2002-03 Superliga", "Mattias Jonson (11)", "Winner", "EC1 qual 3rd round\\nEC3 1st round", "Danish Supercup winner"], ["1990-91", "1", "1991 Superliga", "Bent Christensen (11)", "Semi-final", "EC3 semi-final", ""], ["1981-82", "4", "1982 1st Division", "Michael Laudrup (15)", "4th round", "", ""], ["1993-94", "3", "1993-94 Superliga", "Mark Strudal (13)", "Winner", "EC3 3rd round", ""], ["2003-04", "2", "2003-04 Superliga", "Thomas Kahlenberg (11)", "Semi-final", "EC3 3rd round", ""], ["1988-89", "2", "1989 1st Division", "Bent Christensen (10)", "Winner", "EC1 1st round", ""], ["1991-92", "7", "1991-92 Superliga", "Kim Vilfort (9)", "4th round", "EC1 2nd round", ""], ["1992-93", "3", "1992-93 Superliga", "Kim Vilfort (10)", "5th round", "", ""]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many seasons was morten rasmussen the top scorer? | 4 | 128 | Answer: |
Table InputTable: [["Nr.", "Name", "Area (km²)", "Population (2006)", "Capital", "Club(s)"], ["3", "Cairo", "3,435", "7,786,640", "Cairo", "Al-Ahly - Al Mokawloon - ENPPI - El-Jaish - El-Shorta - Itesalat"], ["1", "Alexandria", "2,900", "4,110,015", "Alexandria", "Al Itthad Al Sakandary - El-Olympi - Haras El Hodood"], ["4", "Gharbia", "25,400", "3,790,670", "Tanta", "Ghazl El-Mehalla"], ["6", "Ismailia", "1,442", "942,832", "Ismailia", "Ismaily"], ["6", "Suez", "17,840", "510,935", "Suez", "Petrojet"], ["5", "Giza", "85,153", "6,272,571", "Giza", "Zamalek- Tersana"], ["7", "Port Said", "72", "570,768", "Port Said", "Al Masry"], ["2", "Asyut", "25,926", "3,441,597", "Asyut", "Petrol Asyout"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:which governorates have areas larger than 10,000 km? | Asyut, Gharbia, Giza, Suez | 128 | Answer: |
Table InputTable: [["Release date", "Album Title", "Record Company", "Song Title", "Certification"], ["December 2007", "H.O.P.E. (Healing Of Pain and Enlightenment)", "Star Records", "\"Count On Me\"", "PARI: Gold"], ["January 2011", "OPM Number 1's Vol. 2", "Star Records", "\"All Me (Remix)\"", "PARI:"], ["April 2009", "OPM Number 1's", "Star Records", "\"Can't Hurry Love\"", "PARI:"], ["February 2011", "I Love You", "Star Records", "\"Catch Me I'm Falling\"", "PARI:"], ["June 2011", "Bida Best Hits Da Best", "Star Records", "\"Mahal Kita Kasi\", \"Catch Me I'm Falling\", \"You Are The One\" with Sam Milby", "PARI:"], ["March 5, 2011", "Kris Aquino: My Heart’s Journey", "Universal Records", "\"God Bless the Broken Road\"", "PARI: Platinum"], ["January 17, 2013", "Himig Handog P-Pop Love Songs 2013", "Star Records", "\"Kahit Na\"", "PARI:"], ["October 8, 2006", "Hotsilog: The ASAP Hotdog Compilation", "ASAP Music", "\"Annie Batungbakal\"", "PARI: Platinum"], ["June 24, 2009", "I Move, I Give, I Love", "Star Records", "\"Power of the Dream\", \"Bagong Umaga\" with Erik Santos & Yeng Constantino", "PARI: Gold"], ["November 12, 2011", "Happy Yipee Yehey! Nananana!", "Star Records", "\"Mahalin Ka Ng Totoo\"", "PARI: Gold"], ["July 25, 2007", "Nagmamahal, Kapamilya: Songs for Global Pinoys", "Star Records", "\"Super Pinoy\"", "PARI: 6X Platinum"], ["June 2010", "60 Taon ng Musika at Soap Opera", "Star Records", "\"Crazy For You\"", "PARI:"], ["November 18, 2011", "Da Best ang Pasko ng Pilipino", "Star Records", "\"Ganyan ang Pasko\", \"Ngayong Pasko Magniningning ang Pilipino\" (Solo) & with Gary Valenciano", "PARI:"], ["November 2010", "Ngayong Pasko Magniningning ang Pilipino: Christmas Songs Compilation", "Star Records", "\"Ganyan ang Pasko\", \"Ngayong Pasko Magniningning ang Pilipino\" (Solo) & with Gary Valenciano", "PARI:"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:the album before the sound count on me | Nagmamahal, Kapamilya: Songs for Global Pinoys | 128 | Answer: |
Table InputTable: [["Season", "Class", "Moto", "Races", "Win", "Podiums", "Pole", "Pts", "Position"], ["2008", "125cc", "Aprilia", "17", "1", "5", "0", "176", "5th"], ["2004", "125cc", "Aprilia", "1", "0", "0", "0", "0", "NC"], ["2007", "125cc", "Derbi", "17", "0", "0", "0", "19", "22nd"], ["2010", "125cc", "Aprilia", "16", "3", "14", "1", "296", "2nd"], ["2011", "125cc", "Aprilia", "16", "8", "11", "7", "302", "1st"], ["2005", "125cc", "Derbi", "13", "0", "0", "0", "1", "36th"], ["2009", "125cc", "Aprilia", "16", "1", "4", "0", "179.5", "3rd"], ["2006", "125cc", "Derbi", "16", "0", "0", "0", "53", "14th"], ["2012", "Moto2", "Suter", "17", "0", "1", "0", "37", "17th"], ["2014", "Moto2", "Suter", "1", "0", "0", "0", "0*", "NC*"], ["2013", "Moto2", "Suter", "17", "3", "4", "1", "150", "7th"], ["Total", "", "", "147", "16", "39", "9", "1213.5", ""]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:in which season has nicolás won the most races? | 2011 | 128 | Answer: |
Table InputTable: [["Year", "Rider", "Victories", "Bike", "Manufacturer's Championship"], ["1996", "Troy Corser", "7", "Ducati 916", "Ducati"], ["2001", "Troy Bayliss", "6", "Ducati 996", "Ducati"], ["1998", "Carl Fogarty", "3", "Ducati 916", "Ducati"], ["1994", "Carl Fogarty", "11", "Ducati 916", "Ducati"], ["1995", "Carl Fogarty", "13", "Ducati 916", "Ducati"], ["1999", "Carl Fogarty", "11", "Ducati 996", "Ducati"], ["2000", "(Colin Edwards)", "(7)", "(Honda RC51)", "Ducati"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:who had more total victories troy corser or troy bayless? | Troy Corser | 128 | Answer: |
Table InputTable: [["No.", "Date", "Tournament", "Surface", "Partnering", "Opponent in the final", "Score"], ["2.", "April 4, 2011", "Chile F2", "Clay", "Sergio Galdós", "Guillermo Hormazábal\\n Rodrigo Pérez", "5–7, 7–6(5), [10–5]"], ["7.", "August 26, 2012", "Ecuador F3", "Clay", "Sergio Galdós", "Mauricio Echazú\\n Guillermo Rivera-Aránguiz", "6-2, 6-1"], ["1.", "September 13, 2010", "Ecuador F2", "Hard", "Roberto Quiroz", "Peter Aarts\\n Christopher Racz", "6–4, 6–4"], ["3.", "April 11, 2011", "Chile F3", "Clay", "Roberto Quiroz", "Luis David Martínez\\n Miguel Ángel Reyes-Varela", "6–4, 7–5"], ["8.", "October 8, 2012", "Chile F8", "Clay", "Gustavo Sterin", "Cristóbal Saavedra-Corvalán\\n Guillermo Rivera-Aránguiz", "6-4, 7-5"], ["6.", "August 20, 2012", "Colombia F2", "Clay", "Ariel Behar", "Nicolas Barrientos\\n Michael Quintero", "2-1 Ret."], ["9.", "May 13, 2013", "Argentina F6", "Clay", "Sergio Galdós", "Franco Agamenone\\n Jose Angel Carrizo", "4-6, 6-4, [10–1]"], ["4.", "August 8, 2011", "Peru F1", "Clay", "Sergio Galdós", "Martín Cuevas\\n Guido Pella", "6–4, 6–0"], ["5.", "August 5, 2012", "Manta", "Hard", "Renzo Olivo", "Víctor Estrella\\n João Souza", "6–3, 6–0"], ["10.", "May 27, 2013", "Argentina F8", "Clay", "Sergio Galdós", "Daniel Dutra da Silva\\n Pablo Galdón", "6-0, 7-5"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what is the name of the last tournament on the chart? | Argentina F8 | 128 | Answer: |
Table InputTable: [["Year", "Wins (majors)", "Earnings ($)", "Rank"], ["2008", "1", "1,695,237", "48"], ["1995", "1", "1,111,999", "6"], ["2009", "0", "1,622,401", "52"], ["1998", "1", "1,541,152", "11"], ["2007", "0", "1,016,489", "96"], ["2012", "0", "989,753", "100"], ["2005", "0", "2,658,779", "13"], ["2011", "0", "1,056,300", "88"], ["1990", "1", "537,172", "20"], ["1987", "1", "297,378", "33"], ["2013", "0", "303,470", "165"], ["2001", "1", "3,169,463", "5"], ["1986", "0", "113,245", "77"], ["1999", "0", "2,475,328", "3"], ["2010", "0", "1,214,472", "73"], ["2000", "0", "2,337,765", "9"], ["Career*", "20 (1)", "42,511,946", "6"], ["2004", "0", "3,075,092", "10"], ["1996", "1", "1,211,139", "7"], ["1991", "1", "686,361", "8"], ["2006", "1", "2,747,206", "16"], ["1985", "0", "0", "-"], ["1997", "2 (1)", "1,635,953", "3"], ["1992", "3", "1,191,630", "2"], ["2002", "0", "2,056,160", "21"], ["1988", "0", "156,068", "75"], ["1989", "0", "278,760", "44"], ["1994", "0", "474,219", "33"], ["2003", "4", "6,081,896", "3"], ["1993", "2", "777,059", "12"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what was the first year that had over $1,000,000 in earnings? | 1992 | 128 | Answer: |
Table InputTable: [["Year", "Winner", "Age", "Jockey", "Trainer", "Owner", "Distance\\n(Miles)", "Time", "Purse", "Gr"], ["2012", "Take Charge Indy", "3", "Calvin Borel", "Patrick B. Byrne", "C & M Sandford", "1-1/8", "1:48.79", "$1,000,000", "I"], ["1971", "Eastern Fleet", "3", "Eddie Maple", "Reggie Cornell", "Calumet Farm", "1-1/8", "1:47.40", "", ""], ["1981", "Lord Avie", "3", "Chris McCarron", "Daniel Perlsweig", "David Simon", "1-1/8", "1:50.40", "$250,000", "I"], ["1999", "Vicar", "3", "Shane Sellers", "Carl Nafzger", "James B. Tafel", "1-1/8", "1:50.83", "$750,000", "I"], ["1958", "Tim Tam", "3", "Bill Hartack", "Horace A. Jones", "Calumet Farm", "1-1/8", "1:49.20", "", ""], ["1982", "Timely Writer", "3", "Jeffrey Fell", "Dominic Imprescia", "Peter & Francis Martin", "1-1/8", "1:49.60", "$250,000", "I"], ["1955", "Nashua", "3", "Eddie Arcaro", "Jim Fitzsimmons", "Belair Stud", "1-1/8", "1:53.20", "", ""], ["1965", "Native Charger", "3", "John L. Rotz", "Ray Metcalf", "Warner Stable", "1-1/8", "1:51.20", "", ""], ["1960", "Bally Ache", "3", "Bobby Ussery", "Homer Pitt", "Edgehill Farm", "1-1/8", "1:47.60", "", ""], ["2003", "Empire Maker", "3", "Jerry Bailey", "Robert Frankel", "Juddmonte Farms", "1-1/8", "1:49.05", "$1,000,000", "I"], ["1956", "Needles", "3", "David Erb", "Hugh L. Fontaine", "D & H Stable", "1-1/8", "1:48.60", "", ""], ["1959", "Easy Spur", "3", "Bill Hartack", "Paul L. Kelley", "Spring Hill Farm", "1-1/8", "1:47.20", "", ""], ["2005", "High Fly", "3", "Jerry Bailey", "Nick Zito", "Live Oak Plantation", "1-1/8", "1:49.43", "$1,000,000", "I"], ["1968", "Forward Pass", "3", "Don Brumfield", "Henry Forrest", "Calumet Farm", "1-1/8", "1:49.00", "", ""], ["2000", "Hal's Hope", "3", "Roger Velez", "Harold Rose", "Rose Family Stable", "1-1/8", "1:51.49", "$1,000,000", "I"], ["1962", "Ridan", "3", "Manuel Ycaza", "LeRoy Jolley", "Jolley / Woods / Greer", "1-1/8", "1:50.40", "", ""], ["1961", "Carry Back", "3", "Johnny Sellers", "Jack A. Price", "Mrs. Katherine Price", "1-1/8", "1:48.80", "", ""], ["1964", "Northern Dancer", "3", "Bill Shoemaker", "Horatio Luro", "Windfields Farm", "1-1/8", "1:50.80", "", ""], ["1967", "In Reality", "3", "Earlie Fires", "Melvin Calvert", "Frances A. Genter", "1-1/8", "1:50.20", "", ""], ["1969", "Top Knight", "3", "Manuel Ycaza", "Ray Metcalf", "Steven B. Wilson", "1-1/8", "1:48.40", "", ""], ["1966", "Williamston Kid †", "3", "Robert Stevenson", "James Bartlett", "Ternes & Bartlett", "1-1/8", "1:50.60", "", ""]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what is the time difference between take charge indy and vicar? | 2.04 | 128 | Answer: |
Table InputTable: [["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"], ["3", "South Korea (KOR)", "32", "48", "65", "145"], ["6", "North Korea (PRK)", "6", "10", "20", "36"], ["2", "Japan (JPN)", "46", "56", "77", "179"], ["8", "Mongolia (MGL)", "1", "1", "6", "8"], ["7", "Hong Kong (HKG)", "2", "2", "9", "13"], ["1", "China (CHN)", "127", "63", "33", "223"], ["4", "Chinese Taipei (TPE)", "12", "34", "26", "72"], ["Total", "Total", "237", "230", "254", "721"], ["9", "Guam (GUM)", "0", "0", "1", "1"], ["5", "Macau (MAC)", "11", "16", "17", "44"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what number of gold medals were won by south korea? | 32 | 128 | Answer: |
Table InputTable: [["Rank", "Player", "Nation", "Club", "Goals"], ["6", "Andriy Budnyy", "UKR", "Wilmington Hammerheads", "8"], ["1", "Jhonny Arteaga", "COL", "FC New York", "13"], ["2", "Matthew Delicâte", "ENG", "Richmond Kickers", "10"], ["6", "Andrew Welker", "USA", "Harrisburg City Islanders", "8"], ["6", "Jamie Watson", "USA", "Orlando City", "8"], ["9", "Chris Banks", "USA", "Wilmington Hammerheads", "7"], ["9", "George Davis IV", "USA", "Dayton Dutch Lions", "7"], ["3", "José Angulo", "USA", "Harrisburg City Islanders", "9"], ["9", "Sallieu Bundu", "SLE", "Charlotte Eagles", "7"], ["3", "Luke Mulholland", "ENG", "Wilmington Hammerheads", "9"], ["9", "Sainey Touray", "GAM", "Harrisburg City Islanders", "7"], ["3", "Maxwell Griffin", "USA", "Orlando City", "9"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many total goals did the sixth ranked players score? | 24 | 128 | Answer: |
Table InputTable: [["Week", "Date", "TV Time", "Opponent", "Result", "Attendance"], ["12", "November 22, 1998", "FOX 11:00 am MT", "at Washington Redskins", "W 45–42", "63,435"], ["16", "December 20, 1998", "FOX 2:05 pm MT", "New Orleans Saints", "W 19–17", "51,617"], ["13", "November 29, 1998", "FOX 11:00 am MT", "at Kansas City Chiefs", "L 34–24", "69,613"], ["1", "September 6, 1998", "FOX 1:05 pm MT", "at Dallas Cowboys", "L 38–10", "63,602"], ["4", "September 27, 1998", "FOX 10:00 am MT", "at St. Louis Rams", "W 20–17", "55,832"], ["6", "October 11, 1998", "FOX 1:05 pm MT", "Chicago Bears", "W 20–7", "50,495"], ["2", "September 13, 1998", "FOX 1:15 pm MT", "at Seattle Seahawks", "L 33–14", "57,678"], ["3", "September 20, 1998", "ESPN 5:15 pm MT", "Philadelphia Eagles", "W 17–3", "39,782"], ["9", "November 1, 1998", "FOX 11:00 am MT", "at Detroit Lions", "W 17–15", "66,087"], ["10", "November 8, 1998", "FOX 2:15 pm MT", "Washington Redskins", "W 29–27", "45,950"], ["5", "October 4, 1998", "CBS 1:05 pm MT", "Oakland Raiders", "L 23–20", "53,240"], ["14", "December 6, 1998", "FOX 2:15 pm MT", "New York Giants", "L 23–19", "46,128"], ["15", "December 13, 1998", "FOX 11:00 am MT", "at Philadelphia Eagles", "W 20–17", "62,176"], ["7", "October 18, 1998", "FOX 10:00 am MT", "at New York Giants", "L 34–7", "70,456"], ["17", "December 27, 1998", "CBS 2:05 pm MT", "San Diego Chargers", "W 16–13", "71,670"], ["11", "November 15, 1998", "FOX 2:15 pm MT", "Dallas Cowboys", "L 35–28", "71,670"], ["8", "Bye", "Bye", "Bye", "Bye", ""]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many games had more than 60,000 in attendance in the 1998 regular season? | 8 | 128 | Answer: |
Table InputTable: [["Pos", "Rider", "Manufactuer", "Time/Retired", "Points"], ["22", "Massimo Pennacchioli", "Honda", "+1:59.498", ""], ["20", "Gabriele Debbia", "Honda", "+1:40.049", ""], ["1", "Doriano Romboni", "Honda", "33:53.776", "25"], ["21", "Adrian Bosshard", "Honda", "+1:47.492", ""], ["24", "Alessandro Gramigni", "Gilera", "+1 Lap", ""], ["15", "Juan Borja", "Honda", "+1:15.769", "1"], ["17", "Adi Stadler", "Honda", "+1:16.349", ""], ["11", "Alberto Puig", "Honda", "+25.136", "5"], ["19", "Paolo Casoli", "Gilera", "+1:26.061", ""], ["2", "Loris Capirossi", "Honda", "+0.090", "20"], ["10", "Luis d'Antin", "Honda", "+25.044", "6"], ["16", "Frédéric Protat", "Aprilia", "+1:15.858", ""], ["Ret", "Volker Bähr", "Honda", "Retirement", ""], ["3", "Helmut Bradl", "Honda", "+0.384", "16"], ["9", "Carlos Cardús", "Honda", "+4.893", "7"], ["12", "John Kocinski", "Suzuki", "+25.463", "4"], ["8", "Jean-Philippe Ruggia", "Aprilia", "+3.985", "8"], ["14", "Jean-Michel Bayle", "Aprilia", "+1:15.546", "2"], ["4", "Max Biaggi", "Honda", "+2.346", "13"], ["23", "Bernard Haenggeli", "Aprilia", "+2:41.806", ""], ["18", "Bernd Kassner", "Aprilia", "+1:16:464", ""], ["Ret", "Jean-Pierre Jeandat", "Aprilia", "Retirement", ""], ["Ret", "Patrick van den Goorbergh", "Aprilia", "Retirement", ""], ["Ret", "Jurgen van den Goorbergh", "Aprilia", "Retirement", ""], ["13", "Jochen Schmid", "Yamaha", "+47.065", "3"], ["Ret", "Andreas Preining", "Aprilia", "Retirement", ""], ["Ret", "Luis Maurel", "Aprilia", "Retirement", ""], ["6", "Tetsuya Harada", "Yamaha", "+2.537", "10"], ["Ret", "Wilco Zeelenberg", "Aprilia", "Retirement", ""], ["5", "Loris Reggiani", "Aprilia", "+2.411", "11"], ["DNS", "Nobuatsu Aoki", "Honda", "Did not start", ""], ["Ret", "Eskil Suter", "Aprilia", "Retirement", ""], ["7", "Pierfrancesco Chili", "Yamaha", "+3.845", "9"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:who is the first rider? | Doriano Romboni | 128 | Answer: |
Table InputTable: [["Year", "Chassis", "Engine", "Start", "Finish", "Team"], ["2009", "Dallara", "Honda", "1", "1", "Team Penske"], ["2007", "Dallara", "Honda", "1", "3", "Team Penske"], ["2006", "Dallara", "Honda", "2", "25", "Team Penske"], ["2008", "Dallara", "Honda", "4", "4", "Team Penske"], ["2010", "Dallara", "Honda", "1", "9", "Team Penske"], ["2011", "Dallara", "Honda", "16", "17", "Team Penske"], ["2002", "Dallara", "Chevrolet", "13", "1", "Team Penske"], ["2003", "Dallara", "Toyota", "1", "2", "Team Penske"], ["2001", "Dallara", "Oldsmobile", "11", "1", "Team Penske"], ["2005", "Dallara", "Toyota", "5", "9", "Team Penske"], ["2012", "Dallara", "Chevrolet", "6", "10", "Team Penske"], ["2004", "Dallara", "Toyota", "8", "9", "Team Penske"], ["2013", "Dallara", "Chevrolet", "8", "6", "Team Penske"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what number of times did team penske start in the 1st position? | 4 | 128 | Answer: |
Table InputTable: [["Ship", "Hull No.", "Status", "Years Active", "NVR\\nPage"], ["Henry J. Kaiser", "T-AO-187", "Active—Southern California Duty Oiler", "1986–present", "AO187"], ["Walter S. Diehl", "T-AO-193", "Active", "1988–present", "AO193"], ["Kanawha", "T-AO-196", "Active", "1991–present", "AO196"], ["Henry Eckford", "T-AO-192", "Cancelled when 84% complete,\\ntransferred to the Maritime Administration,\\nlaid up in the James River Reserve Fleet,\\nscrapped in 2011", "Launched 1989, never in service", "AO192"], ["Leroy Grumman", "T-AO-195", "Active", "1989–present", "AO195"], ["Big Horn", "T-AO-198", "Active", "1992–present", "AO198"], ["Andrew J. Higgins", "T-AO-190", "Inactivated May 1996. Sold to the Chilean Navy May 2009. Towed to Atlantic Marine Alabama shipyard, Mobile, Alabama, September 2009 for three-month refit. Commissioned in Chilean Navy on 10 February 2010 and renamed Almirante Montt.[1]", "1987-1996 (USA); 2010–present (Chile)", "AO190"], ["Benjamin Isherwood", "T-AO-191", "Cancelled when 95.3% complete,\\ntransferred to the Maritime Administration,\\nlaid up in the James River Reserve Fleet,\\nscrapped in 2011", "Launched 1988, christened 1991, never in service", "AO191"], ["Tippecanoe", "T-AO-199", "Active", "1993–present", "AO199"], ["John Lenthall", "T-AO-189", "Active", "1987-1996; 1998–present", "AO189"], ["Guadalupe", "T-AO-200", "Active", "1992–present", "AO200"], ["Laramie", "T-AO-203", "Active", "1996–present", "AO203"], ["Patuxent", "T-AO-201", "Active", "1995–present", "AO201"], ["Rappahannock", "T-AO-204", "Active", "1995–present", "AO204"], ["Pecos", "T-AO-197", "Active", "1990–present", "AO197"], ["Yukon", "T-AO-202", "Active", "1994–present", "AO202"], ["Joshua Humphreys", "T-AO-188", "Inactivated 1996, returned to service 2005", "1987-1996; 2005-2006; 2010-present", "AO188"], ["John Ericsson", "T-AO-194", "Active", "1991–present", "AO194"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how long has the henry j. kaiser been active? | 28 | 128 | Answer: |
Table InputTable: [["Pos", "Team", "Played", "Won", "Draw", "Lost", "Goals For", "Goals Against", "Goal Difference", "Points", "Notes"], ["4", "ÍBV", "18", "8", "5", "5", "29", "17", "+12", "29", "Inter-Toto Cup"], ["1", "KR", "18", "11", "4", "3", "27", "14", "+13", "37", "UEFA Champions League"], ["3", "Grindavík", "18", "8", "6", "4", "25", "18", "+7", "30", "UEFA Cup"], ["10", "Leiftur", "18", "3", "7", "8", "24", "39", "-15", "16", "Relegated"], ["2", "Fylkir", "18", "10", "5", "3", "39", "16", "+23", "35", "UEFA Cup"], ["9", "Stjarnan", "18", "4", "5", "9", "18", "31", "-13", "17", "Relegated"], ["6", "Keflavík", "18", "4", "7", "7", "21", "35", "-14", "19", ""], ["8", "Fram", "18", "4", "5", "9", "22", "33", "-11", "17", ""], ["5", "ÍA", "18", "7", "5", "6", "21", "17", "+4", "26", ""], ["7", "Breiðablik", "18", "5", "3", "10", "29", "35", "-6", "18", ""]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what is the number of teams? | 10 | 128 | Answer: |
Table InputTable: [["Player", "League", "Cup", "Europa\\nLeague", "Total"], ["Marquinhos", "9", "1", "3", "13"], ["Aleksandar Tonev", "2", "0", "0", "2"], ["Stanislav Kostov", "1", "0", "0", "1"], ["Pavel Vidanov", "0", "0", "1", "1"], ["Tomislav Kostadinov", "0", "0", "1", "1"], ["Spas Delev", "13", "7", "2", "22"], ["Emil Gargorov", "2", "0", "0", "2"], ["Michel Platini", "10", "0", "0", "10"], ["Boris Galchev", "1", "0", "0", "1"], ["Rumen Trifonov", "2", "0", "1", "3"], ["Todor Yanchev", "0", "1", "1", "2"], ["Kostadin Stoyanov", "1", "0", "0", "1"], ["Christian Tiboni", "0", "0", "1", "1"], ["Apostol Popov", "2", "0", "0", "2"], ["Giuseppe Aquaro", "3", "0", "2", "5"], ["Cillian Sheridan", "4", "2", "1", "7"], ["Gregory Nelson", "3", "0", "1", "4"], ["Total", "53", "11", "14", "78"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many points does marquinhos and tonev have together? | 15 | 128 | Answer: |
Table InputTable: [["Heat", "Rank", "Name", "Result", "Notes"], ["1", "9", "Libbie Hickman (USA)", "15:30.56 q", "SB"], ["2", "27", "Amy Rudolph (USA)", "16:00.87", ""], ["1", "2", "Paula Radcliffe (GBR)", "15:27.25 Q", ""], ["1", "21", "Melody Fairchild (USA)", "15:47.66", ""], ["1", "36", "Martha Portobanco (NCA)", "19:08.44", ""], ["2", "10", "Lydia Cheromei (KEN)", "15:32.00 Q", ""], ["1", "5", "Roberta Brunet (ITA)", "15:29.03 Q", ""], ["1", "29", "Laurence Duquenoy (FRA)", "16:06.02", ""], ["1", "16", "Yelena Kopytova (RUS)", "15:37.19", "PB"], ["1", "15", "Kate Anderson (AUS)", "15:36.16 q", ""], ["2", "32", "Helena Javornik (SLO)", "16:28.38", ""], ["1", "19", "Adriana Fernandez (MEX)", "15:41.55", ""], ["1", "1", "Gabriela Szabo (ROU)", "15:26.62 Q", ""], ["2", "3", "Fernanda Ribeiro (POR)", "15:27.30 Q", ""], ["1", "33", "Justine Nahimana (BUR)", "17:21.77", ""], ["2", "17", "Sonia O'Sullivan (IRL)", "15:40.82", ""], ["2", "31", "Jelena Chelnova (LAT)", "16:27.63", ""], ["2", "—", "Carol Howe (CAN)", "DNS", ""], ["2", "20", "Olivera Jevtić (YUG)", "15:43.76", ""], ["1", "28", "Genet Gebregiorgis (ETH)", "16:04.40", "SB"], ["2", "—", "Annemari Sandell (FIN)", "DNS", ""], ["1", "23", "Marina Bastos (POR)", "15:54.01", ""], ["1", "4", "Harumi Hiroyama (JPN)", "15:27.75 Q", ""], ["2", "18", "Stela Olteanu (ROU)", "15:40.86", ""], ["1", "35", "Zalia Aliou (TOG)", "18:34.45", "NR"], ["2", "6", "Liu Jianying (CHN)", "15:29.28 Q", "PB"], ["2", "—", "Kristina da Fonseca-Wollheim (GER)", "DNF", ""], ["1", "25", "Valerie Vaughan (IRL)", "15:57.58", ""], ["1", "26", "Zohra Ouaziz (MAR)", "15:58.84", ""], ["1", "8", "Li Wei (CHN)", "15:29.62 Q", ""], ["1", "34", "Nebiat Habtemariam (ERI)", "18:26.50", ""], ["1", "—", "Elana Meyer (RSA)", "DNS", ""], ["2", "13", "Naoko Takahashi (JPN)", "15:32.25 Q", ""], ["2", "24", "Restituta Joseph (TAN)", "15:55.22", "NR"], ["2", "—", "Anne Hare (NZL)", "DNF", ""], ["2", "14", "Yuko Kawakami (JPN)", "15:32.71 Q", ""], ["2", "11", "Merima Denboba (ETH)", "15:32.01 Q", ""], ["2", "22", "Chrystosomia Iakovou (GRE)", "15:51.14", ""], ["1", "12", "Gunhild Hall (NOR)", "15:32.13 q", ""], ["1", "7", "Ayelech Worku (ETH)", "15:29.37 Q", ""], ["2", "30", "Una English (IRL)", "16:07.09", ""]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:who was the american to get 9th place? | Libbie Hickman | 128 | Answer: |
Table InputTable: [["Represents", "Contestant", "Age", "Height", "Hometown"], ["Panamá Centro", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Panamá Este", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Panamá Oeste", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Bocas del Toro", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Herrera", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Veraguas", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Chiriquí Occidente", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Chiriquí", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Comarcas", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Los Santos", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Darién", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Colón", "TBD", "TBD", "0.0 m (0 in)", "TBD"], ["Coclé", "TBD", "TBD", "0.0 m (0 in)", "TBD"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many cities are listed that contain the word "panama"? | 3 | 128 | Answer: |
Table InputTable: [["Heat", "Rank", "Name", "Result", "Notes"], ["1", "8", "Li Wei (CHN)", "15:29.62 Q", ""], ["2", "—", "Anne Hare (NZL)", "DNF", ""], ["2", "24", "Restituta Joseph (TAN)", "15:55.22", "NR"], ["2", "6", "Liu Jianying (CHN)", "15:29.28 Q", "PB"], ["2", "—", "Kristina da Fonseca-Wollheim (GER)", "DNF", ""], ["2", "13", "Naoko Takahashi (JPN)", "15:32.25 Q", ""], ["1", "4", "Harumi Hiroyama (JPN)", "15:27.75 Q", ""], ["1", "21", "Melody Fairchild (USA)", "15:47.66", ""], ["1", "29", "Laurence Duquenoy (FRA)", "16:06.02", ""], ["2", "14", "Yuko Kawakami (JPN)", "15:32.71 Q", ""], ["1", "35", "Zalia Aliou (TOG)", "18:34.45", "NR"], ["2", "10", "Lydia Cheromei (KEN)", "15:32.00 Q", ""], ["2", "20", "Olivera Jevtić (YUG)", "15:43.76", ""], ["2", "18", "Stela Olteanu (ROU)", "15:40.86", ""], ["1", "26", "Zohra Ouaziz (MAR)", "15:58.84", ""], ["2", "32", "Helena Javornik (SLO)", "16:28.38", ""], ["1", "15", "Kate Anderson (AUS)", "15:36.16 q", ""], ["2", "17", "Sonia O'Sullivan (IRL)", "15:40.82", ""], ["1", "33", "Justine Nahimana (BUR)", "17:21.77", ""], ["1", "—", "Maysa Matrood (IRQ)", "DNS", ""], ["1", "19", "Adriana Fernandez (MEX)", "15:41.55", ""], ["1", "9", "Libbie Hickman (USA)", "15:30.56 q", "SB"], ["1", "25", "Valerie Vaughan (IRL)", "15:57.58", ""], ["2", "—", "Carol Howe (CAN)", "DNS", ""], ["1", "—", "Elana Meyer (RSA)", "DNS", ""], ["2", "30", "Una English (IRL)", "16:07.09", ""], ["2", "27", "Amy Rudolph (USA)", "16:00.87", ""], ["2", "31", "Jelena Chelnova (LAT)", "16:27.63", ""], ["1", "16", "Yelena Kopytova (RUS)", "15:37.19", "PB"], ["1", "1", "Gabriela Szabo (ROU)", "15:26.62 Q", ""], ["2", "—", "Annemari Sandell (FIN)", "DNS", ""], ["1", "28", "Genet Gebregiorgis (ETH)", "16:04.40", "SB"], ["1", "34", "Nebiat Habtemariam (ERI)", "18:26.50", ""], ["1", "5", "Roberta Brunet (ITA)", "15:29.03 Q", ""], ["1", "12", "Gunhild Hall (NOR)", "15:32.13 q", ""], ["1", "7", "Ayelech Worku (ETH)", "15:29.37 Q", ""], ["2", "3", "Fernanda Ribeiro (POR)", "15:27.30 Q", ""], ["2", "11", "Merima Denboba (ETH)", "15:32.01 Q", ""], ["1", "23", "Marina Bastos (POR)", "15:54.01", ""], ["2", "22", "Chrystosomia Iakovou (GRE)", "15:51.14", ""], ["1", "36", "Martha Portobanco (NCA)", "19:08.44", ""]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what country does li wei represent? | China | 128 | Answer: |
Table InputTable: [["Year", "Designer(s)", "Brief description", "Selected by:", "Associated publication"], ["1979", "Jean Muir\\nManolo Blahnik for Zapata (shoes)", "Black rayon jersey dress & beret with black leather jacket", "Geraldine Ranson", "The Sunday Telegraph"], ["1964", "Jean Muir for Jane & Jane\\nCharles Jourdan for Dior (shoes)", "Dress in printed Liberty silk", "Members of The Fashion Writers' Association", ""], ["1983", "Sheridan Barnett\\nManolo Blahnik (shoes)", "Linen dress and coat", "Sally Brampton", "The Observer"], ["1978", "Female: Gordon Luke Clarke\\nMale: Cerruti", "Female: Printed cotton & polyester jersey tunic, skirt and trousers worn with black leather skirt and coat\\nMale: Coat, jacket, waistcoat & trousers, knitted wool and wool tweed", "Barbara Griggs", "The Daily Mail"], ["1987", "John Galliano\\nPatrick Cox (shoes)", "Checked cotton coat, skirt, shirt & hat", "Debbi Mason", "Elle"], ["1963", "Mary Quant\\nReed Crawford (hat)\\nAnello & Davide (boots)", "Grey wool 'Rex Harrison' pinafore dress & cream blouse", "Members of The Fashion Writers' Association", ""], ["1969", "Ossie Clark for Quorum\\nRayne (shoes)", "Woman's silk chiffon and satin trouser suit in Celia Birtwell print", "Prudence Glynn", "The Times"], ["1965", "John Bates for Jean Varon\\nAnello & Davide (shoes)", "Printed linen dress with mesh midriff", "Members of The Fashion Writers' Association", ""], ["1968", "Jean Muir\\nBally (shoes)", "Black-spotted white cotton voile dress", "Ailsa Garland", "Fashion Magazine"], ["1972", "Teenage girl:Biba\\nYoung girl: Bobby Hillson\\nYoung boy: Orange Hand for Montague Burton", "Teenage girl: Dress, hat & boots, all in red & white spotted cotton\\nYoung girl: Checked cotton dress & pinafore\\nYoung boy: Trousers, jumper and tank top", "Moira Keenan", "The Sunday Times"], ["1980", "Calvin Klein\\nDiego della Valle (sandals)", "Red & brown striped silk dress with leather belt & wooden jewellery", "Michael Roberts", "The Sunday Times"], ["1967", "David Bond for Slimma\\nEdward Mann (hat)\\nSaxone (shoes)", "Woman's trouser suit, hat & blouse in striped cotton", "Felicity Green", "The Daily Mirror"], ["1966", "Michèle Rosier of V de V (coat)\\nYoung Jaeger (dress)\\nSimone Mirman (hat)\\nElliott (boots)\\nJohn Bates for Echo (tights)", "Clear plastic raincoat and boots worn with black & white rayon linen dress, white tights and white hat with red plastic visor", "Ernestine Carter", "The Sunday Times"], ["1997", "Female: Hussein Chalayan\\nFemale: Julien MacDonald\\nFemale: Lainey Keogh\\nFemale: Deborah Milner\\nPhilip Treacy (bonnet)", "Female: Purple evening dress with sunburst bead embroidery (Chalayan)\\nFemale: 'Mermaid' evening dress, gold knitted rayon & horsehair (MacDonald)\\nFemale: Evening dress and coat, black knit with beading (Keogh)\\nFemale: Evening coat, purple velvet, with fur collar (Milner)\\nSculptural black bonnet", "Isabella Blow", "The Sunday Times"], ["1985", "Female: Bruce Oldfield\\nCharles Jourdan (shoes)\\nMaria Buck (jewellery)\\nMale: Scott Crolla", "Female: Black silk & gold lamé evening dress\\nMale: Shirt, crushed velvet trousers and ikat mules", "Suzy Menkes", "The Times"], ["1984", "Female: BodyMap\\nFemale: Betty Jackson\\nBrian Bolger: (scarf)\\nMale: Katharine Hamnett", "Female: Ensemble comprising skirt, jumper, stockings, hat, waxed jacket & earrings (BodyMap)\\nFemale: Dress, cardigan & hat and scarf (Jackson & Bolger)\\nMale: T-shirt, shirt and cotton trousers", "Brenda Polan", "The Guardian"], ["1971", "Female: Graziella Fontana for Judith Hornby\\nRavel (sandals)\\nMale: Rupert Lycett Green for Blades", "Female: Hot pants suit in checked Liberty cotton\\nMale: Black velvet evening suit & boots", "Serena Sinclair and Patrick Lichfield", "The Daily Telegraph"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:the consecutive year after 1969 | 1970 | 128 | Answer: |
Table InputTable: [["Site", "Municipality", "Comments", "Coordinates", "Type", "Ref."], ["Jōdo-ji Gardens\\n浄土寺庭園\\nJōdoji teien", "Onomichi", "", "34°24′44″N 133°12′36″E / 34.41222952°N 133.21012266°E", "1", "[6]"], ["*Itsukushima\\n厳島\\nItsukushima", "Hatsukaichi", "also a Special Historic Site; Itsukushima Jinja is inscribed on the UNESCO World Heritage List", "34°16′16″N 132°18′22″E / 34.27116774°N 132.30612348°E", "8", "[3]"], ["Peace Memorial Park\\n平和記念公園\\nHeiwa kinen kōen", "Hiroshima", "the Hiroshima Peace Memorial (Genbaku Dome) is inscribed on the UNESCO World Heritage List", "34°23′34″N 132°27′09″E / 34.39284707°N 132.45251203°E", "1", "[8]"], ["Kikkawa Motoharu Fortified Residence Gardens\\n吉川元春館跡庭園\\nKikkawa Motoharu yakata ato teien", "Kitahiroshima", "", "34°43′01″N 132°27′58″E / 34.71697004°N 132.46599393°E", "1", "[1]"], ["Former Mantoku-in Gardens\\n旧万徳院庭園\\nkyū-Mantokuin teien", "Kitahiroshima", "", "34°43′27″N 132°28′22″E / 34.72423174°N 132.47265069°E", "1", "[2]"], ["Shukkei-en\\n縮景園\\nShukukei-en", "Hiroshima", "", "34°24′02″N 132°28′04″E / 34.40050182°N 132.46770735°E", "1", "[5]"], ["Tomo Park\\n鞆公園\\nTomo kōen", "Fukuyama", "", "34°23′01″N 133°23′48″E / 34.3835209°N 133.39662133°E", "1, 8", "[9]"], ["Taishaku-kyō\\n帝釈川の谷 (帝釈峡)\\nTaishaku-gawa no tani (Taishaku-kyō)", "Shōbara/Jinsekikōgen", "", "34°50′58″N 133°13′23″E / 34.8493628°N 133.2231609°E", "5, 6", "[7]"], ["*Sandan-kyō\\n三段峡\\nSandan-kyō", "Akiōta/Kitahiroshima", "", "34°36′57″N 132°11′44″E / 34.61573328°N 132.19561853°E", "3, 5, 6", "[4]"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:how many of these sites do not have images? | 3 | 128 | Answer: |
Table InputTable: [["Country", "Amphibians", "Birds", "Mammals", "Reptile", "Total terrestrial vertebrates", "Vascular plants", "Biodiversity"], ["Nicaragua", "61", "632", "181", "178", "1052", "7590", "8642"], ["Costa Rica", "183", "838", "232", "258", "1511", "12119", "13630"], ["Guatemala", "133", "684", "193", "236", "1246", "8681", "9927"], ["Honduras", "101", "699", "201", "213", "1214", "5680", "6894"], ["El Salvador", "30", "434", "137", "106", "707", "2911", "3618"], ["Belize", "46", "544", "147", "140", "877", "2894", "3771"], ["Panama", "182", "904", "241", "242", "1569", "9915", "11484"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:which country in central america is the most biodiverse? | Costa Rica | 128 | Answer: |
Table InputTable: [["Year", "Competition", "Venue", "Position", "Event", "Notes"], ["2006", "African Championships", "Bambous, Mauritius", "13th (h)", "800 m", "2:10.50"], ["2008", "African Championships", "Addis Ababa, Ethiopia", "6th", "800 m", "2:05.95"], ["2010", "African Championships", "Nairobi, Kenya", "7th", "800 m", "2:08.45"], ["2009", "World Championships", "Berlin, Germany", "36th (h)", "800 m", "2:06.72"], ["2006", "Commonwealth Games", "Melbourne, Australia", "9th (sf)", "800 m", "2:01.84"], ["2011", "All-Africa Games", "Maputo, Mozambique", "12th (h)", "800 m", "2:06.72"], ["2003", "All-Africa Games", "Abuja, Nigeria", "11th (h)", "800 m", "2:05.19"], ["2007", "All-Africa Games", "Algiers, Algeria", "1st", "800 m", "2:02.83"], ["2006", "Lusophony Games", "Macau", "1st", "800 m", "2:07.34"], ["2009", "Lusophony Games", "Lisbon, Portugal", "4th", "800 m", "2:07.48"], ["2010", "Commonwealth Games", "Delhi, India", "–", "800 m", "DNF"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:in which years did piuza not participate in a competition during her career? | 2004, 2005 | 128 | Answer: |
Table InputTable: [["Name", "Character", "Unicode code point (decimal)", "Standard", "DTD", "Old ISO subset", "Description"], ["lt", "<", "U+003C (60)", "HTML 2.0", "HTMLspecial", "ISOnum", "less-than sign"], ["minus", "−", "U+2212 (8722)", "HTML 4.0", "HTMLsymbol", "ISOtech", "minus sign"], ["plusmn", "±", "U+00B1 (177)", "HTML 3.2", "HTMLlat1", "ISOnum", "plus-minus sign (plus-or-minus sign)"], ["gt", ">", "U+003E (62)", "HTML 2.0", "HTMLspecial", "ISOnum", "greater-than sign"], ["divide", "÷", "U+00F7 (247)", "HTML 3.2", "HTMLlat1", "ISOnum", "division sign (obelus)"], ["le", "≤", "U+2264 (8804)", "HTML 4.0", "HTMLsymbol", "ISOtech", "less-than or equal to"], ["not", "¬", "U+00AC (172)", "HTML 3.2", "HTMLlat1", "ISOnum", "not sign"], ["radic", "√", "U+221A (8730)", "HTML 4.0", "HTMLsymbol", "ISOtech", "square root (radical sign)"], ["micro", "µ", "U+00B5 (181)", "HTML 3.2", "HTMLlat1", "ISOnum", "micro sign"], ["times", "×", "U+00D7 (215)", "HTML 3.2", "HTMLlat1", "ISOnum", "multiplication sign"], ["yen", "¥", "U+00A5 (165)", "HTML 3.2", "HTMLlat1", "ISOnum", "yen sign (yuan sign)"], ["uuml", "ü", "U+00FC (252)", "HTML 2.0", "HTMLlat1", "ISOlat1", "Latin small letter u with diaeresis"], ["cent", "¢", "U+00A2 (162)", "HTML 3.2", "HTMLlat1", "ISOnum", "cent sign"], ["para", "¶", "U+00B6 (182)", "HTML 3.2", "HTMLlat1", "ISOnum", "pilcrow sign (paragraph sign)"], ["sigma", "σ", "U+03C3 (963)", "HTML 4.0", "HTMLsymbol", "ISOgrk3", "Greek small letter sigma"], ["reg", "®", "U+00AE (174)", "HTML 3.2", "HTMLlat1", "ISOnum", "registered sign (registered trademark symbol)"], ["pound", "£", "U+00A3 (163)", "HTML 3.2", "HTMLlat1", "ISOnum", "pound sign"], ["acirc", "â", "U+00E2 (226)", "HTML 2.0", "HTMLlat1", "ISOlat1", "Latin small letter a with circumflex"], ["sigmaf", "ς", "U+03C2 (962)", "HTML 4.0", "HTMLsymbol", "ISOgrk3", "Greek small letter final sigma"], ["lowast", "∗", "U+2217 (8727)", "HTML 4.0", "HTMLsymbol", "ISOtech", "asterisk operator"], ["nu", "ν", "U+03BD (957)", "HTML 4.0", "HTMLsymbol", "ISOgrk3", "Greek small letter nu"], ["sect", "§", "U+00A7 (167)", "HTML 3.2", "HTMLlat1", "ISOnum", "section sign"], ["chi", "χ", "U+03C7 (967)", "HTML 4.0", "HTMLsymbol", "ISOgrk3", "Greek small letter chi"], ["sup1", "¹", "U+00B9 (185)", "HTML 3.2", "HTMLlat1", "ISOnum", "superscript one (superscript digit one)"], ["deg", "°", "U+00B0 (176)", "HTML 3.2", "HTMLlat1", "ISOnum", "degree symbol"], ["ouml", "ö", "U+00F6 (246)", "HTML 2.0", "HTMLlat1", "ISOlat1", "Latin small letter o with diaeresis"]] | You are a question-answering model specialized in tabular data.
I will provide you with a table in a list-of-lists format (where the first row is the header) and a single natural language question.
Your task is to extract the exact table cell value(s) that directly answer the provided question. Follow these guidelines:
- Output Format:
Your response must be a plain text string. If the answer contains multiple values, separate them by a comma followed by a space (for example: Netherlands, Italy).
- Direct Answers Only:
Return ONLY the table cell value(s) that directly answer the question. Do not include headers, column names, or any additional text.
- Aggregation Requirements:
If the question requires an aggregation (e.g., average, sum, count, etc.), return only the aggregated value(s) as a plain text string.
- No Explanations:
Do NOT provide any explanations, reasoning, or repeat these instructions in your answer.
Examples:
Example 1
Table:
[["Model", "Production_Years", "Engine", "Displacement", "Power", "Top_Speed"],
["11/18 PS", "1907–1910", "4 inline", "2,799 cc", "13.2 kW (18 PS)", "55 km/h (34 mph)"],
["13/30 PS", "1909–1912", "4 inline", "3,180 cc", "25.7 kW (35 PS)", ""],
["K 5/13 PS", "1911–1920", "4 inline", "1,292 cc", "9.6–10.3 kW (13–14 PS)", "55 km/h (34 mph)"],
["10/28 PS", "1909–1912", "4 inline", "2,612 cc", "22 kW (30 PS)", ""],
["30/70 PS", "1911–1914", "4 inline", "7,853 cc", "51 kW (70 PS)", "115 km/h (71 mph)"],
["35/80 PS", "1911–1914", "4 inline", "9,081 cc", "62.5 kW (85 PS)", ""]]
Question: which typ(s) had the longest construction times?
Answer: K 5/13 PS
Example 2
Table:
[["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"],
[1, "Netherlands", 20, 9, 0, 29],
[2, "Italy", 10, 15, 3, 28],
[3, "Belgium", 1, 2, 6, 9],
[4, "Spain", 1, 1, 13, 15],
[5, "Great Britain", 0, 2, 0, 2],
[6, "Germany", 0, 1, 7, 8],
[7, "Greece", 0, 1, 0, 1],
[7, "Russia", 0, 1, 0, 1],
[9, "Sweden", 0, 0, 2, 2],
[10, "France", 0, 0, 1, 1]]
Question: name the countries that had at least 5 gold medals
Answer: Netherlands, Italy
Question:what is the code for the less than sign? | U+003C (60) | 128 | Answer: |
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