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
value |
|---|---|---|---|---|
Table InputTable: [["Rank", "Name", "Nationality", "Time"], ["8", "Edith Arraspide", "Argentina", "5:13.95"], ["", "Joanne Malar", "Canada", "4:43.64"], ["7", "Carolyn Adel", "Suriname", "5:13.24"], ["5", "Fabíola Molina", "Brazil", "5:03.43"], ["", "Jenny Kurth", "United States", "4:57.24"], ["", "Alison Fealey", "United States", "4:48.31"], ["4", "Sonia Fonseca", "Puerto Rico", "5:03.15"], ["6", "Isabel Rojas", "Colombia", "5:11.58"]] | 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:from which country did the last place swimmer come? | Argentina | 128 | Answer: |
Table InputTable: [["Week", "Date", "TV Time", "Opponent", "Result", "Attendance"], ["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"], ["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"], ["15", "December 13, 1998", "FOX 11:00 am MT", "at Philadelphia Eagles", "W 20–17", "62,176"], ["5", "October 4, 1998", "CBS 1:05 pm MT", "Oakland Raiders", "L 23–20", "53,240"], ["12", "November 22, 1998", "FOX 11:00 am MT", "at Washington Redskins", "W 45–42", "63,435"], ["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"], ["3", "September 20, 1998", "ESPN 5:15 pm MT", "Philadelphia Eagles", "W 17–3", "39,782"], ["17", "December 27, 1998", "CBS 2:05 pm MT", "San Diego Chargers", "W 16–13", "71,670"], ["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"], ["14", "December 6, 1998", "FOX 2:15 pm MT", "New York Giants", "L 23–19", "46,128"], ["11", "November 15, 1998", "FOX 2:15 pm MT", "Dallas Cowboys", "L 35–28", "71,670"], ["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:who was the first game against? | Dallas Cowboys | 128 | Answer: |
Table InputTable: [["#", "Stadium", "Capacity", "City", "Region", "Home Team", "Opened"], ["4", "Parc des Princes", "48,712", "Paris", "Île-de-France", "Paris Saint-Germain FC", "1897"], ["27", "Stade Sébastien Charléty", "20,000", "Paris", "Île-de-France", "Paris FC", "1938"], ["1", "Stade de France", "81,338", "Paris", "Île-de-France", "France national football team", "1998"], ["22", "Stade Michel d'Ornano", "21,500", "Caen", "Lower Normandy", "Stade Malherbe Caen", "1993"], ["7", "Stade de la Beaujoire", "38,285", "Nantes", "Pays de la Loire", "FC Nantes Atlantique", "1984"], ["10", "Stadium Municipal", "35,575", "Toulouse", "Midi-Pyrénées", "Toulouse FC", "1937"], ["14", "Stade de la Meinau", "29,230", "Strasbourg", "Alsace", "RC Strasbourg", "1914"], ["12", "Stade de la Mosson", "32,939", "Montpellier", "Languedoc-Roussillon", "Montpellier HSC", "1972"], ["19", "Stade de l'Abbé-Deschamps", "23,467", "Auxerre", "Bourgogne", "AJ Auxerre", "1918"], ["11", "Stade Chaban-Delmas", "34,462", "Bordeaux", "Aquitaine", "FC Girondins de Bordeaux", "1938"], ["26", "Stade Auguste Bonal", "20,025", "Montbéliard", "Franche-Comté", "FC Sochaux-Montbéliard", "2000"], ["21", "Stade Auguste-Delaune", "21,684", "Reims", "Champagne-Ardenne", "Stade Reims", "1935"], ["20", "Stade Louis Dugauguez", "23,189", "Sedan", "Champagne-Ardenne", "Club Sportif Sedan Ardennes", "2000"], ["23", "Stade de l'Aube", "20,400", "Troyes", "Champagne-Ardenne", "Troyes AC", "1956"], ["16", "Grand Stade du Havre", "25,178", "Le Havre", "Upper Normandy", "Le Havre AC", "2012"], ["25", "Stade des Alpes", "20,068", "Grenoble", "Rhône-Alpes", "Grenoble Foot 38", "2008"], ["3", "Grand Stade Lille Métropole", "50,186", "Villeneuve-d'Ascq", "Nord-Pas-de-Calais", "Lille OSC", "2012"], ["15", "Stade Municipal Saint-Symphorien", "26,700", "Metz", "Lorraine", "FC Metz", "1923"], ["13", "Stade de la Route de Lorient", "31,127", "Rennes", "Brittany", "Stade Rennais FC", "1912"], ["6", "Stade Gerland", "41,044", "Lyon", "Rhône-Alpes", "Olympique Lyonnais", "1926"], ["8", "Stade Geoffroy-Guichard", "37,587", "Saint-Étienne", "Rhône-Alpes", "AS Saint-Étienne", "1931"], ["2", "Stade Vélodrome", "60,013", "Marseille", "Provence-Alpes-Côte d'Azur", "Olympique de Marseille", "1937"], ["18", "Stade du Hainaut", "24,926", "Valenciennes", "Nord-Pas-de-Calais", "Valenciennes FC", "2011"], ["5", "Stade Félix Bollaert", "41,233", "Lens", "Nord-Pas-de-Calais", "RC Lens", "1932"], ["17", "MMArena", "25,000", "Le Mans", "Pays de la Loire", "Le Mans UC", "2011"], ["24", "Stade Marcel Picot", "20,087", "Tomblaine", "Lorraine", "AS Nancy", "1926"]] | 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 capacity of the stadiums in paris? | 150050 | 128 | Answer: |
Table InputTable: [["No.", "Date", "Tournament", "Winning score", "Margin of\\nvictory", "Runner(s)-up"], ["3", "25 Apr 2004", "Tsuruya Open", "−9 (64-73-69-69=275)", "2 strokes", "Keiichiro Fukabori, Scott Laycock,\\n Tatsuya Mitsuhashi, Taichi Teshima,\\n Shinichi Yokota"], ["11", "15 Apr 2012", "Token Homemate Cup", "−15 (68-69-70-62=269)", "2 strokes", "Ryuichi Oda"], ["9", "26 Sep 2010", "Asia-Pacific Panasonic Open\\n(co-sanctioned by the Asian Tour)", "−6 (71-70-66=207)", "1 stroke", "Ryuichi Oda"], ["6", "22 Apr 2007", "Tsuruya Open", "−16 (67-65-68-68=268)", "2 strokes", "Masahiro Kuramoto, Hirofumi Miyase,\\n Takuya Taniguchi"], ["5", "23 Apr 2006", "Tsuruya Open", "−11 (70-68-66-69=273)", "2 strokes", "Mamo Osanai"], ["8", "2 Dec 2007", "Golf Nippon Series JT Cup", "−11 (70-70-68-61=261)", "1 stroke", "Toru Taniguchi"], ["1", "3 Nov 2002", "Philip Morris K.K. Championship", "−19 (65-67-67-70=269)", "2 strokes", "Toshimitsu Izawa"], ["4", "26 Jun 2004", "Gateway to the Open Mizuno Open", "−14 (67-68-70-69=274)", "Playoff", "Hiroaki Iijima"], ["12", "29 Jul 2012", "Sun Chlorella Classic", "−15 (69-66-68-70=273)", "2 strokes", "Lee Seong-ho, Hideki Matsuyama (am),\\n Yoshinobu Tsukada"], ["10", "1 May 2011", "The Crowns", "−9 (67-66-68-70=271)", "Playoff", "Jang Ik-jae"], ["7", "11 Nov 2007", "Mitsui Sumitomo VISA Taiheiyo Masters", "−13 (67-68-69-70=274)", "5 strokes", "Toru Taniguchi"], ["2", "10 Aug 2003", "Sun Chlorella Classic", "−8 (71-73-68-68=280)", "1 stroke", "Daisuke Maruyama, Taichi Teshima"], ["13", "30 Jun 2013", "Gateway to the Open Mizuno Open", "−19 (67-66-68-68=269)", "3 strokes", "Kim Kyung-tae"]] | 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 tournament had the least winning score? | Asia-Pacific Panasonic Open (co-sanctioned by the Asian Tour) | 128 | Answer: |
Table InputTable: [["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"], ["5", "Switzerland (SUI)", "0", "2", "1", "3"], ["7", "Great Britain (GBR)", "0", "0", "1", "1"], ["3", "Germany (EUA)", "1", "0", "1", "2"], ["2", "Italy (ITA)", "1", "1", "1", "3"], ["1", "Australia (AUS)", "2", "1", "0", "3"], ["4", "Soviet Union (URS)", "1", "0", "0", "1"], ["7", "France (FRA)", "0", "0", "1", "1"], ["6", "United States (USA)", "0", "1", "0", "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:which country won a silver but didn't win a gold or bronze? | United States (USA) | 128 | Answer: |
Table InputTable: [["Match no.", "Match Type", "Team Europe", "Score", "Team USA", "Progressive Total"], ["30", "Baker", "Team Europe", "202 - 203", "Team USA", "15 - 15"], ["31", "Singles", "Tore Torgersen", "202 - 264", "Chris Barnes", "15 - 16"], ["32", "Singles", "Osku Palermaa", "196 - 235", "Tommy Jones", "15 - 17"], ["26", "Singles", "Osku Palermaa", "217 - 244", "Tommy Jones", "14 - 12"], ["28", "Singles", "Tore Torgersen", "206 - 275", "Doug Kent", "15 - 13"], ["29", "Singles", "Tomas Leandersson", "176 - 258", "Bill Hoffman", "15 - 14"], ["25", "Singles", "Mika Koivuniemi", "217 - 279", "Chris Barnes", "14 - 11"], ["27", "Singles", "Paul Moor", "210 - 199", "Tim Mack", "15 - 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 total number of matches did team usa win after match no. 27? | 5 | 128 | Answer: |
Table InputTable: [["Fence", "Name", "Jockey", "Age", "Handicap (st-lb)", "Starting price", "Fate"], ["22", "Kapeno", "David Dick", "8", "11-6", "100/8", "Fell"], ["26", "Rondetto", "Jeff King", "9", "11-6", "100/8", "Fell"], ["06", "Nedsmar", "John Hudson", "11", "10-13", "100/1", "Fell"], ["18", "Leedsy", "George Robinson", "7", "10-13", "18/1", "Fell"], ["24", "Pontin-Go", "Johnny Lehane", "13", "10-13", "50/1", "Fell"], ["09", "Groomsman", "Beltrán Osorio", "10", "10-13", "100/1", "Fell"], ["01", "Ayala", "Stan Mellor", "11", "10-13", "50/1", "Fell"], ["?", "Black Spot", "J Gamble", "8", "10-13", "100/1", "Fell"], ["06", "Forgotten Dreams", "R Coonan", "11", "11-0", "22/1", "Fell"], ["17", "Bold Biri", "Michael Scudamore", "9", "10-13", "100/1", "Fell"], ["?", "Blonde Warrior", "Mr D Crossley-Cooke", "13", "10-13", "100/1", "Fell"], ["06", "Barleycroft", "Phil Harvey", "10", "10-13", "100/1", "Brought Down"], ["06", "Sizzle-On", "P Hurley", "9", "10/13", "100/1", "Brought Down"], ["04", "Red Tide", "Johnny Haine", "8", "10-13", "33/1", "Fell"], ["06", "Crobeg", "Mr Macer Gifford", "12", "10-13", "100/1", "Brought Down"], ["03", "Ronald's Boy", "Mr Gay Kindersley", "8", "11-1", "100/1", "Fell"], ["?", "Time", "Mr Brough Scott", "10", "10-13", "40/1", "Fell"], ["10", "Dark Venetian", "Jim Renfree", "10", "10-13", "100/1", "Fell"], ["13", "Phebu", "J Morrissey", "8", "10-13", "33/1", "Brought Down"], ["?", "Leslie", "P Jones", "9", "10-13", "33/1", "Pulled Up"], ["06", "Ruby Glen", "Stephen Davenport", "10", "10-13", "33/1", "Brought Down"], ["?", "Solonace", "RW Jones", "13", "10-13", "100/1", "Pulled Up"], ["?", "Reproduction", "Robin Langley", "12", "10-13", "40/1", "Pulled-Up"], ["?", "Vulcano", "Tommy Carberry", "7", "10-13", "50/1", "Pulled Up"], ["?", "Fearless Cavalier", "R West", "14", "10-13", "100/1", "Refused"], ["?", "Quintin Bay", "Pat Taaffe", "9", "10-13", "25/1", "Pulled Up"], ["04", "Cutlette", "M Roberts", "8", "10-13", "50/1", "Pulled Up"], ["?", "Mr McTaffy", "T Jackson", "13", "10-13", "100/1", "Pulled Up"], ["08", "Coleen Star", "Johnny Leech", "11", "10-13", "100/1", "Refused"], ["22", "Ballygowan", "A Redmond", "11", "10-13", "66/1", "Refused"], ["?", "Lizawake", "Mr George Hartigan", "12", "10-13", "100/1", "Pulled Up"], ["?", "French Cottage", "Mr WA Tellwright", "13", "10-13", "100/1", "Refused"], ["?", "Sword Flash", "T Ryan", "12", "10-13", "100/1", "Pulled Up"]] | 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 jockeys whose fate was falling? | 15 | 128 | Answer: |
Table InputTable: [["#", "Episode", "Air date", "Rating", "Share", "18-49 (Rating/Share)", "Viewers (m)", "Rank (night)", "Rank (timeslot)", "Rank (overall)"], ["3", "\"Blind Dates and Bleeding Hearts\"", "October 26, 2007", "6.1", "11", "1.9/6", "8.90", "#1", "#1", "#41"], ["7", "\"The Past Comes Back to Haunt You\"", "November 16, 2007", "6.2", "11", "1.7/5", "8.94", "#4", "#1", "#41"], ["2", "\"Train In Vain\"", "October 19, 2007", "6.5", "12", "2.0/6", "9.69", "#2", "#1", "#37"], ["6", "\"Play Through the Pain\"", "November 15, 2007", "6.1", "10", "3.3/9", "8.93", "#8", "#3", "#45"], ["13", "\"Never Tell\"", "May 13, 2008", "5.8", "10", "2.1/6", "8.46", "", "#2", ""], ["10", "\"FBI Guy\"", "January 4, 2008", "5.2", "9", "1.8/5", "7.68", "#2", "#1", "#36"], ["4", "\"Grannies, Guns, Love Mints\"", "November 2, 2007", "6.4", "11", "1.9/6", "9.47", "#1", "#1", "#35"], ["1", "\"Welcome to the Club\"", "October 12, 2007", "7.3", "13", "2.5/8", "10.82", "#1", "#1", "#26"], ["9", "\"To Drag & To Hold\"", "December 7, 2007", "5.8", "10", "1.8/5", "8.58", "#2", "#1", "#32"], ["5", "\"Maybe, Baby\"", "November 9, 2007", "6.5", "11", "2.0/6", "9.70", "#1", "#1", "#36"], ["8", "\"No Opportunity Necessary\"", "November 23, 2007", "5.3", "9", "1.6/5", "7.76", "#3", "#1", "#45"], ["11", "\"Father's Day\"", "April 29, 2008", "5.8", "9", "1.9/5", "8.14", "#7", "#2", "#42"], ["12", "\"And the Truth Will (Sometimes) Set You Free\"", "May 6, 2008", "6.1", "10", "2.2/6", "8.68", "#8", "#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:which episode had the least number of viewers? | "FBI Guy" | 128 | Answer: |
Table InputTable: [["School", "2007", "2008", "2009", "2010", "2011"], ["Oscar De La Hoya Animo Charter High School", "662", "726", "709", "710", "744"], ["Francisco Bravo Medical Magnet High School", "807", "818", "815", "820", "832"], ["Santee Education Complex", "", "502", "521", "552", "565"], ["James A. Garfield High School", "553", "597", "593", "632", "705"], ["Thomas Jefferson High School", "457", "516", "514", "546", "546"], ["Marc and Eva Stern Math and Science School", "718", "792", "788", "788", "809"], ["Woodrow Wilson High School", "582", "585", "600", "615", "636"], ["Abraham Lincoln High School", "594", "609", "588", "616", "643"], ["Theodore Roosevelt High School", "557", "551", "576", "608", ""]] | 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 index of the oscar de la hoya amino charter school? | 710.2 | 128 | Answer: |
Table InputTable: [["Year", "Class", "No", "Tyres", "Car", "Team", "Co-Drivers", "Laps", "Pos.", "Class\\nPos."], ["1978", "S\\n+2.0", "10", "", "Mirage M9\\nRenault 2.0L Turbo V6", "Grand Touring Cars Inc.", "Vern Schuppan\\n Sam Posey", "293", "10th", "5th"], ["1993", "GT", "71", "D", "Venturi 500LM\\nRenault PRV 3.0 L Turbo V6", "Jacadi Racing", "Michel Maisonneuve\\n Christophe Dechavanne", "210", "DNF", "DNF"], ["1994", "GT2", "49", "P", "Porsche 911 Carrera RSR\\nPorsche 3.8 L Flat-6", "Larbre Compétition", "Jacques Alméras\\n Jean-Marie Alméras", "94", "DNF", "DNF"], ["1972", "S\\n3.0", "22", "", "Ligier JS2\\nMaserati 3.0L V6", "Automobiles Ligier", "Pierre Maublanc", "195", "DNF", "DNF"], ["1977", "S\\n+2.0", "8", "", "Renault Alpine A442\\nRenault 2.0L Turbo V6", "Renault Sport", "Patrick Depailler", "289", "DNF", "DNF"], ["1996", "GT1", "38", "M", "McLaren F1 GTR\\nBMW S70 6.1L V12", "Team Bigazzi SRL", "Steve Soper\\n Marc Duez", "318", "11th", "9th"], ["1974", "S\\n3.0", "15", "", "Ligier JS2\\nMaserati 3.0L V6", "Automobiles Ligier", "Alain Serpaggi", "310", "8th", "5th"], ["1990", "C1", "6", "G", "Porsche 962C\\nPorsche Type-935 3.0L Turbo Flat-6", "Joest Porsche Racing", "Henri Pescarolo\\n Jean-Louis Ricci", "328", "14th", "14th"], ["1973", "S\\n3.0", "62", "", "Ligier JS2\\nMaserati 3.0L V6", "Automobiles Ligier", "Guy Ligier", "24", "DSQ", "DSQ"]] | 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 laps total are listed? | 2061 | 128 | Answer: |
Table InputTable: [["#", "Weekend End Date", "Film", "Box Office"], ["17", "April 26, 1998", "U.S. Marshals", "£780,012"], ["15", "April 12, 1998", "Titanic", "£1,373,363"], ["7", "February 15, 1998", "Titanic", "£3,849,120"], ["9", "March 1, 1998", "Titanic", "£3,403,199"], ["11", "March 15, 1998", "Titanic", "£2,469,191"], ["14", "April 5, 1998", "Titanic", "£1,504,551"], ["16", "April 19, 1998", "Titanic", "£981,940"], ["12", "March 22, 1998", "Titanic", "£1,953,082"], ["13", "March 29, 1998", "Titanic", "£2,223,046"], ["8", "February 22, 1998", "Titanic", "£3,657,613"], ["6", "February 8, 1998", "Titanic", "£4,274,375"], ["4", "January 25, 1998", "Titanic", "£4,805,270"], ["10", "March 8, 1998", "Titanic", "£3,010,921"], ["5", "February 1, 1998", "Titanic", "£4,773,404"], ["37", "September 13, 1998", "Saving Private Ryan", "£2,704,522"], ["38", "September 20, 1998", "Saving Private Ryan", "£2,077,362"], ["1", "January 4, 1998", "Starship Troopers", "£2,221,631"], ["18", "May 3, 1998", "Scream 2", "£2,493,950"], ["31", "August 2, 1998", "Lost in Space", "£3,127,079"], ["41", "October 11, 1998", "The Truman Show", "£2,210,999"], ["30", "July 26, 1998", "Godzilla", "£2,145,088"], ["42", "October 18, 1998", "The Truman Show", "£1,687,037"], ["33", "August 16, 1998", "Armageddon", "£2,243,095"], ["19", "May 10, 1998", "Scream 2", "£1,213,184"], ["43", "October 25, 1998", "Small Soldiers", "£1,137,725"], ["24", "June 14, 1998", "The Wedding Singer", "£974,719"], ["49", "December 6, 1998", "Rush Hour", "£1,809,093"], ["50", "December 13, 1998", "Rush Hour", "£1,179,123"], ["34", "August 23, 1998", "The X-Files", "£2,506,148"], ["29", "July 19, 1998", "Godzilla", "£4,176,960"], ["23", "June 7, 1998", "The Wedding Singer", "£1,031,660"], ["20", "May 17, 1998", "Deep Impact", "£1,763,805"], ["44", "November 1, 1998", "The Exorcist", "£2,186,977"], ["32", "August 9, 1998", "Armageddon", "£2,732,785"], ["35", "August 30, 1998", "The X-Files", "£1,192,131"], ["51", "December 20, 1998", "Rush Hour", "£744,783"], ["25", "June 21, 1998", "City of Angels", "£1,141,654"], ["3", "January 18, 1998", "The Devil's Advocate", "£1,300,773"], ["2", "January 11, 1998", "The Jackal", "£1,422,193"], ["52", "December 27, 1998", "Enemy of the State", "£1,420,216"], ["22", "May 31, 1998", "Deep Impact", "£1,070,805"]] | 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 titanic or u.s. marshals the number one film in week number 17? | U.S. Marshals | 128 | Answer: |
Table InputTable: [["Wrestler:", "Reigns:", "Date:", "Place:", "Notes:"], ["Lanny Poffo", "1", "May 10, 1978", "San Francisco, California", "Defeated Joe Banek to become the first champion"], ["Randy Savage", "1", "March 13, 1979", "Halifax, Nova Scotia", ""], ["Randy Savage", "2", "1981", "Unknown", ""], ["Randy Savage", "3", "1982", "Unknown", ""], ["Paul Christy", "1", "November 13, 1983", "Springfield, Illinois", ""], ["Lanny Poffo", "3", "1981", "Unknown", ""], ["Lanny Poffo", "2", "July 21, 1979", "Lexington, Kentucky", ""], ["Lanny Poffo", "4", "January 1, 1984", "Springfield, Illinois", ""]] | 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 last reigning champion? | Lanny Poffo | 128 | Answer: |
Table InputTable: [["Week", "Date", "TV Time", "Opponent", "Result", "Attendance"], ["11", "November 15, 1998", "FOX 2:15 pm MT", "Dallas Cowboys", "L 35–28", "71,670"], ["1", "September 6, 1998", "FOX 1:05 pm MT", "at Dallas Cowboys", "L 38–10", "63,602"], ["13", "November 29, 1998", "FOX 11:00 am MT", "at Kansas City Chiefs", "L 34–24", "69,613"], ["4", "September 27, 1998", "FOX 10:00 am MT", "at St. Louis Rams", "W 20–17", "55,832"], ["15", "December 13, 1998", "FOX 11:00 am MT", "at Philadelphia Eagles", "W 20–17", "62,176"], ["9", "November 1, 1998", "FOX 11:00 am MT", "at Detroit Lions", "W 17–15", "66,087"], ["12", "November 22, 1998", "FOX 11:00 am MT", "at Washington Redskins", "W 45–42", "63,435"], ["2", "September 13, 1998", "FOX 1:15 pm MT", "at Seattle Seahawks", "L 33–14", "57,678"], ["10", "November 8, 1998", "FOX 2:15 pm MT", "Washington Redskins", "W 29–27", "45,950"], ["3", "September 20, 1998", "ESPN 5:15 pm MT", "Philadelphia Eagles", "W 17–3", "39,782"], ["16", "December 20, 1998", "FOX 2:05 pm MT", "New Orleans Saints", "W 19–17", "51,617"], ["17", "December 27, 1998", "CBS 2:05 pm MT", "San Diego Chargers", "W 16–13", "71,670"], ["14", "December 6, 1998", "FOX 2:15 pm MT", "New York Giants", "L 23–19", "46,128"], ["6", "October 11, 1998", "FOX 1:05 pm MT", "Chicago Bears", "W 20–7", "50,495"], ["5", "October 4, 1998", "CBS 1:05 pm MT", "Oakland Raiders", "L 23–20", "53,240"], ["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:who did the cardinals play after dallas cowboys in november 15, 1998? | at Washington Redskins | 128 | Answer: |
Table InputTable: [["Year", "Title", "Rol", "Format", "Related Links"], ["2008", "Me Mueves 1° Temporada", "Arturo", "Serie", "https://www.youtube.com/watch?v=lj2Q6rrOK1k"], ["2009", "Me Mueves 2° Temporada", "Arturo", "Serie", "https://www.youtube.com/watch?v=fNE5WHZ_tt8&feature=related"], ["1997–2001", "Bizbirije", "Differentes personajes en \"No es Justo\" y \"Ponte Bizbo\"", "Capsulas", "https://www.youtube.com/watch?v=M7YCuvFbFJ0"], ["2006–2008", "Skimo", "Fito", "Serie", "https://www.youtube.com/watch?v=w_6cn3BajE0&feature=related"], ["2005", "Cuentos De Pelos", "", "Serie", ""], ["2010", "Niñas mal (telenovela)", "Piti", "Telenovela", "http://www.novelamtv.com/"], ["2004", "El Divan De Valentina", "Dj", "Serie", ""], ["2010", "Soy tu fan (México)", "Actor", "Serie", "https://www.youtube.com/watch?v=hnCb5GG0E2U"], ["2006", "La Vida Inmune", "Malhora", "Feature Film", "https://www.youtube.com/watch?v=PQt4RU3usnw"], ["2008", "La Carretera Es Blanca Y Llana", "Peru", "Short Film", "https://www.youtube.com/watch?v=GKehxHZ16rs"], ["2009", "Infinito", "", "Short Film", ""], ["2007", "La Zona", "Alejandro", "Feature Film", "https://www.youtube.com/watch?v=W5SzXJe-NMk"], ["2001", "Perros Patinadores", "", "Short Film", ""], ["2005", "Quinceañera", "", "Feature Film", ""], ["2010", "Yo Te Estaré Cuidando", "Camilo", "Short Film", ""]] | 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:nameeach of the formats in the table. | Telenovela, Serie, Short Film, Feature Film, Capsulas | 128 | Answer: |
Table InputTable: [["No. in\\nseason", "No. in\\nseries", "Title", "Canadian airdate", "US airdate", "Production code"], ["18", "335", "\"Better Man\"", "February 4, 2014", "February 4, 2014", "1318"], ["30", "347", "\"Sparks Will Fly\" Part Two", "April 22, 2014", "April 22, 2014", "1330"], ["13", "330", "\"Who Do You Think You Are\"", "October 31, 2013", "October 31, 2013", "1313"], ["29", "346", "\"Sparks Will Fly\" Part One", "April 15, 2014", "April 15, 2014", "1329"], ["39", "356", "\"Thunderstruck\" Part One", "July 29, 2014", "July 29, 2014", "1339"], ["4", "321", "\"My Own Worst Enemy\"", "July 25, 2013", "July 25, 2013", "1304"], ["19", "336", "\"Dig Me Out\"", "February 11, 2014", "February 11, 2014", "1319"], ["9", "326", "\"This Is How We Do It\"", "October 3, 2013", "October 3, 2013", "1309"], ["10", "327", "\"You Got Me\"", "October 10, 2013", "October 10, 2013", "1310"], ["8", "325", "\"Young Forever\"", "August 22, 2013", "August 22, 2013", "1308"], ["14", "331", "\"Barely Breathing\"", "November 7, 2013", "November 7, 2013", "1314"], ["12", "329", "\"Everything You've Done Wrong\"", "October 24, 2013", "October 24, 2013", "1312"], ["26", "343", "\"Close to Me\"", "March 25, 2014", "March 25, 2014", "1326"], ["11", "328", "\"You Oughta Know\"", "October 17, 2013", "October 17, 2013", "1311"], ["17", "334", "\"The World I Know\"", "January 28, 2014", "January 28, 2014", "1317"], ["34", "351", "\"My Hero\"", "June 24, 2014", "June 24, 2014", "1334"], ["40", "357", "\"Thundestruck\" Part Two", "July 29, 2014", "July 29, 2014", "1340"], ["32", "349", "\"Enjoy The Silence\"", "June 10, 2014", "June 10, 2014", "1332"], ["31", "348", "\"You Are Not Alone\"", "June 3, 2014", "June 3, 2014", "1331"], ["36", "353", "\"Out Of My Head\"", "July 8, 2014", "July 8, 2014", "1336"], ["20", "337", "\"Power to the People\"", "February 18, 2014", "February 18, 2014", "1320"], ["25", "342", "\"What It's Like\"", "March 18, 2014", "March 18, 2014", "1325"], ["6", "323", "\"Cannonball\"", "August 8, 2013", "August 8, 2013", "1306"], ["23–24", "340–341", "\"Unbelievable\"", "March 11, 2014", "March 11, 2014", "1323 & 1324"], ["33", "350", "\"How Bizarre\"", "June 17, 2014", "June 17, 2014", "1333"], ["21", "338", "\"No Surprises\"", "February 25, 2014", "February 25, 2014", "1321"], ["3", "320", "\"All I Wanna Do\"", "July 18, 2013", "July 18, 2013", "1303"], ["22", "339", "\"Basket Case\"", "March 4, 2014", "March 4, 2014", "1322"], ["27", "344", "\"Army of Me\"", "April 1, 2014", "April 1, 2014", "1327"]] | 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 canadian airdate directly after "better man"? | "Dig Me Out" | 128 | Answer: |
Table InputTable: [["Pick #", "Player", "Position", "Nationality", "NHL team", "College/junior/club team"], ["157", "Petri Skriko", "Right Wing", "Finland", "Vancouver Canucks", "Saipa (Finland)"], ["149", "Rick Zombo", "Defence", "United States", "Detroit Red Wings", "Austin Mavericks (USHL)"], ["162", "Dale DeGray", "Defence", "Canada", "Calgary Flames", "Oshawa Generals (OMJHL)"], ["155", "Mike Sturgeon", "Defence", "Canada", "Edmonton Oilers", "Kelowna Buckaroos (BCJHL)"], ["154", "Mitch Lamoureux", "Centre", "Canada", "Pittsburgh Penguins", "Oshawa Generals (OMJHL)"], ["163", "Steve Taylor", "Left Wing", "United States", "Philadelphia Flyers", "Providence College (ECAC)"], ["161", "Armel Parisee", "Defence", "Canada", "Boston Bruins", "Chicoutimi Saguenéens (QMJHL)"], ["160", "Kari Kanervo", "Centre", "Finland", "Minnesota North Stars", "TPS (Finland)"], ["165", "Dan Brennan", "Left Wing", "Canada", "Los Angeles Kings", "University of North Dakota (WCHA)"], ["148", "Dan McFall", "Defence", "United States", "Winnipeg Jets", "Buffalo Jr. Sabres (NAJHL)"], ["159", "Johan Mellstrom", "Left Wing", "Sweden", "Chicago Black Hawks", "Falun (Sweden)"], ["151", "Denis Dore", "Right Wing", "Canada", "Hartford Whalers", "Chicoutimi Saguenéens (QMJHL)"], ["168", "Bill Dowd", "Defence", "Canada", "New York Islanders", "Ottawa 67's (OMJHL)"], ["164", "Gates Orlando", "Centre", "Canada", "Buffalo Sabres", "Providence College (ECAC)"], ["150", "Tony Arima", "Left Wing", "Finland", "Colorado Rockies", "Jokerit (Finland)"], ["156", "Ari Lahteenmaki", "Right Wing", "Finland", "New York Rangers", "HIFK (Finland)"], ["167", "Alain Vigneault", "Defence", "Canada", "St. Louis Blues", "Trois-Rivières Draveurs (QMJHL)"], ["152", "Gaetan Duchesne", "Left Wing", "Canada", "Washington Capitals", "Quebec Remparts (QMJHL)"], ["166", "Paul Gess", "Left Wing", "United States", "Montreal Canadiens", "Bloomington Jefferson High School (USHS-MN)"], ["153", "Richard Turmel", "Defence", "Canada", "Toronto Maple Leafs", "Shawinigan Cataractes (QMJHL)"], ["158", "Andre Cote", "Right Wing", "Canada", "Quebec Nordiques", "Quebec Remparts (QMJHL)"]] | 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 drafted after petri skriko? | Andre Cote | 128 | Answer: |
Table InputTable: [["Name", "Nationality", "From", "To", "Honours", "Comments"], ["René Heitmann", "Denmark", "17 July 2010", "27 July 2010", "", "Never coached the team in a match"], ["John 'Tune' Kristiansen", "Denmark", "18 June 2012", "23 June 2012", "", "Caretaker for one league match"], ["Johnny Petersen", "Denmark", "5 May 1998", "14 October 2001", "", "Originally had contract until end of 2001"], ["Ebbe Skovdahl", "Denmark", "11 October 2003", "6 November 2005", "Team was relegated to second tier", "Originally had contract until summer 2007"], ["Anders Theil", "Denmark", "7 November 2005", "7 July 2009", "", "Originally had contract until summer 2011"], ["Ole Mørk", "Denmark", "15 October 2001", "10 October 2003", "Won promotion to first tier", "Originally had contract until end of 2004"], ["Christian Andersen", "Denmark", "11 July 2009", "19 June 2010", "Team was relegated to third tier", "Club went bankrupt after the season"], ["Henrik Jensen", "Denmark", "1 July 2012", "Present", "", ""], ["John 'Tune' Kristiansen", "Denmark", "1996", "4 May 1998", "Won promotion to second tier", ""], ["Peer F. Hansen", "Denmark", "1 January 2012", "18 June 2012", "won promotion to the third tier", ""], ["John 'Tune' Kristiansen", "Denmark", "27 July 2010", "30 December 2011", "won promotion to the fourth tier", "Originally had contract until summer 2012"]] | 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 was rené heitmann the head coach of boldklubben frem? | 10 days | 128 | Answer: |
Table InputTable: [["Date", "Opponent#", "Rank#", "Site", "TV", "Result", "Attendance"], ["October 24", "Minnesota", "#6", "Kinnick Stadium • Iowa City, IA (Floyd of Rosedale)", "ABC", "L 10-12", "60,000"], ["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"], ["November 21", "Michigan State", "#19", "Kinnick Stadium • Iowa City, IA", "", "W 36-7", "60,103"], ["October 10", "Indiana", "#15", "Kinnick Stadium • Iowa City, IA", "", "W 42-28", "60,000"], ["January 1", "vs. #12 Washington*", "#13", "Rose Bowl • Pasadena, CA (Rose Bowl)", "NBC", "L 0-28", "105,611"], ["September 26", "#6 UCLA*", "", "Kinnick Stadium • Iowa City, IA", "", "W 20-7", "60,004"], ["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"], ["September 19", "at Iowa State*", "", "Cyclone Stadium • Ames, IA (Cy-Hawk Trophy)", "", "L 12-23", "53,922"], ["October 31", "at Illinois", "#16", "Memorial Stadium • Champaign, IL", "", "L 7-24", "66,877"], ["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:who was the opponent before minnesota? | Michigan | 128 | Answer: |
Table InputTable: [["Season", "Team", "Country", "Division", "Apps", "Goals"], ["2007/08", "Dnipro Dnipropetrovsk", "Ukraine", "1", "24", "0"], ["2008/09", "Dnipro Dnipropetrovsk", "Ukraine", "1", "22", "1"], ["2006/07", "Dnipro Dnipropetrovsk", "Ukraine", "1", "12", "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"], ["2011/12", "Dnipro Dnipropetrovsk", "Ukraine", "1", "16", "0"], ["2006", "Spartak Nizhny Novgorod", "Russia", "2", "36", "1"], ["2005", "CSKA Moscow", "Russia", "1", "0", "0"], ["2004", "CSKA Moscow", "Russia", "1", "0", "0"], ["2012/13", "Lokomotiv Moscow", "Russia", "1", "8", "0"], ["2013/14", "Lokomotiv Moscow", "Russia", "1", "14", "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 are the number of times dnipro dnipropetrovsk is listed as a team on this chart? | 7 | 128 | Answer: |
Table InputTable: [["Place", "Shooter", "5 pts", "4 pts", "3 pts", "2 pts", "1 pts", "0 pts", "Total", "Rank"], ["3", "Dennis Fenton", "1", "2", "5", "2", "-", "-", "32", "16"], ["4", "John O'Leary", "1", "2", "6", "-", "-", "1", "31", "19"], ["4", "John Faunthorpe", "-", "5", "4", "-", "-", "1", "32", "16"], ["6", "László Szomjas", "-", "1", "5", "1", "-", "3", "21", "25"], ["1", "Harald Natvig", "2", "4", "4", "-", "-", "-", "38", "8"], ["4", "Alexander Rogers", "1", "2", "5", "2", "-", "-", "32", "16"], ["6", "Elemér Takács", "1", "2", "3", "-", "1", "3", "23", "23"], ["3", "John Boles", "3", "5", "2", "-", "-", "-", "41", "3"], ["6", "Gusztáv Szomjas", "-", "4", "4", "1", "-", "1", "30", "20"], ["1", "Otto Olsen", "1", "5", "4", "-", "-", "-", "37", "11"], ["2", "Alfred Swahn", "3", "4", "3", "-", "-", "-", "40", "5"], ["3", "Walter Stokes", "1", "6", "3", "-", "-", "-", "38", "8"], ["–", "Miloslav Hlaváč", "1", "2", "6", "1", "-", "-", "33", "15"], ["2", "Otto Hultberg", "1", "6", "3", "-", "-", "-", "38", "8"], ["–", "Czechoslovakia (TCH)", "1", "2", "6", "1", "-", "-", "33", ""], ["4", "Cyril Mackworth-Praed", "6", "-", "3", "1", "-", "-", "41", "3"], ["5", "Martti Liuttula", "3", "2", "3", "2", "-", "-", "36", "14"], ["5", "Jalo Autonen", "1", "1", "5", "2", "-", "1", "28", "21"], ["6", "Hungary (HUN)", "1", "8", "17", "4", "1", "9", "97", ""], ["1", "Einar Liberg", "5", "2", "3", "-", "-", "-", "42", "2"], ["5", "Toivo Tikkanen", "1", "1", "5", "1", "-", "2", "26", "22"], ["3", "Raymond Coulter", "2", "4", "3", "1", "-", "-", "37", "11"], ["4", "Great Britain (GBR)", "8", "9", "18", "3", "-", "2", "136", ""], ["5", "Finland (FIN)", "7", "10", "15", "5", "-", "3", "130", ""], ["2", "Mauritz Johansson", "2", "4", "3", "1", "-", "-", "37", "11"], ["5", "Karl Magnus Wegelius", "2", "6", "2", "-", "-", "-", "40", "5"], ["2", "Fredric Landelius", "2", "5", "3", "-", "-", "-", "39", "7"], ["2", "Sweden (SWE)", "8", "19", "12", "1", "-", "-", "154", ""], ["3", "United States (USA)", "7", "17", "13", "3", "-", "-", "148", ""], ["1", "Ole Lilloe-Olsen", "4", "5", "1", "-", "-", "-", "43", "1"], ["1", "Norway (NOR)", "12", "16", "12", "-", "-", "-", "160", ""], ["6", "Rezső Velez", "-", "1", "5", "2", "-", "2", "23", "23"]] | 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 members were on each team? | 4 | 128 | Answer: |
Table InputTable: [["Year", "Song", "Album", "Position", "Chart"], ["1989", "\"Sunshine\"", "24/7", "23", "Billboard Hot 100"], ["1991", "\"Gentle\"", "Swingin'", "31", "Billboard Hot 100"], ["1989", "\"I Like It\"", "24/7", "7", "Billboard Hot 100"], ["1993", "\"Ooh Child\"", "The Way I Am", "27", "Billboard Hot 100"], ["1987", "\"Summergirls\"", "24/7", "50", "Billboard Hot 100"], ["1990", "\"Romeo\"", "Swingin'", "6", "Billboard Hot 100"], ["1989", "\"24/7\"", "24/7", "42", "Billboard Hot 100"], ["1990", "\"Never 2 Much of U\"", "24/7", "61", "Billboard Hot 100"], ["1993", "\"Endlessly\"", "The Way I Am", "--", "Billboard Hot 100"], ["1989", "\"I Like It\"", "24/7", "3", "Hot Dance Club Play"], ["1989", "\"24/7\"", "24/7", "12", "Hot R&B/Hip-Hop Songs"]] | 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:i "gentle" above or below "sunshine" on the charts? | below | 128 | Answer: |
Table InputTable: [["Name", "Country", "Town", "Height\\nmetres / ft", "Structural type", "Held record", "Notes"], ["Ostankino Tower", "Russia", "Moscow", "540 / 1,772", "Tower", "1967–1976", ""], ["Eiffel Tower", "France", "Paris", "300.6 / 986", "Tower", "1889–1930", "Currently stands at a height of 324 metres (1,063 ft)."], ["Burj Khalifa", "United Arab Emirates", "Dubai", "829.8 / 2,722", "Skyscraper", "2007–present", "Topped-out on 17 January 2009"], ["CN Tower", "Canada", "Toronto", "553 / 1,815", "Tower", "1976–2007", ""], ["St. Mary's Church", "Germany", "Stralsund", "151 / 500", "Church", "1549–1647", "Spire destroyed by lightning in 1647; today stands at a height of 104 metres (341 ft)."], ["Lincoln Cathedral", "England", "Lincoln", "159.7 / 524", "Church", "1311–1549", "Spire collapsed in 1549; today, stands at a height of 83 metres (272 ft)."], ["Great Pyramid of Giza", "Egypt", "Giza", "146 / 480", "Mausoleum", "2570 BC–1311", "Due to erosion today it stands at the height of 138.8 metres (455 ft)."], ["Empire State Building", "United States", "New York City", "448 / 1,472", "Skyscraper", "1931–1967", ""], ["Notre-Dame Cathedral", "France", "Rouen", "151 / 500", "Church", "1876–1880", ""], ["Chrysler Building", "United States", "New York City", "319 / 1,046", "Skyscraper", "1930–1931", ""], ["Strasbourg Cathedral", "Germany and/or France (today France)", "Strasbourg", "142 / 470", "Church", "1647–1874", ""], ["Washington Monument", "United States", "Washington, D.C.", "169.3 / 555", "Monument", "1884–1889", ""], ["Cologne Cathedral", "Germany", "Cologne", "157.4 / 516", "Church", "1880–1884", ""], ["St Nikolai", "Germany", "Hamburg", "147.3 / 483", "Church", "1874–1876", "Due to aerial bombing in World War II the nave was demolished; only the spire remains."]] | 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 was the first to reach new heights with a tower? | France | 128 | Answer: |
Table InputTable: [["#", "Title", "Time", "Lyrics", "Music", "Producer(s)", "Performer(s)"], ["12", "\"Red Mist\"", "3:54", "Boondox", "Mike E. Clark", "Boondox", "Boondox\\nBlaze Ya Dead Homie\\nTwiztid"], ["8", "\"Rollin Hard\"", "4:07", "Boondox", "Mike E. Clark", "Boondox", "Boondox"], ["3", "\"Out Here\"", "3:18", "Boondox", "Mike E. Clark\\nTino Grosse", "Boondox", "Boondox"], ["7", "\"They Pray with Snakes\"", "3:56", "Boondox", "Kuma", "Boondox", "Boondox"], ["9", "\"The Harvest\"", "3:53", "Boondox\\nAMB", "Kuma", "Boondox\\nAMB", "Boondox\\nAxe Murder Boyz"], ["4", "\"It Ain't A Thang\"", "3:45", "Boondox", "Mike E. Clark", "Boondox", "Boondox"], ["13", "\"Angel Like\"", "3:42", "Boondox", "Mike E. Clark", "Boondox", "Boondox"], ["6", "\"Lady In The Jaguar\"", "3:55", "Boondox\\nICP", "Mike E. Clark", "Boondox\\nICP", "Boondox\\nICP"], ["11", "\"Lake of Fire\"", "4:12", "Boondox", "Mike E. Clark", "Boondox", "Boondox"], ["10", "\"Sippin\"", "3:16", "Boondox", "Mike E. Clark", "Boondox", "Boondox"], ["2", "\"Seven\"", "3:30", "Boondox", "Mike E. Clark", "Boondox", "Boondox"], ["5", "\"Digging\"", "3:04", "Boondox", "Kuma", "Boondox", "Boondox"], ["1", "\"Intro\"", "1:16", "", "", "", ""]] | 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 song plays before "red mist"? | Lake of Fire | 128 | Answer: |
Table InputTable: [["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"], ["11", "Kyrgyzstan (KGZ)", "1", "1", "0", "2"], ["8", "Sri Lanka (SRI)", "2", "0", "2", "4"], ["5", "South Korea (KOR)", "3", "2", "1", "6"], ["12", "Kuwait (KUW)", "1", "0", "0", "1"], ["9", "Qatar (QAT)", "1", "4", "3", "8"], ["3", "Saudi Arabia (KSA)", "7", "1", "0", "8"], ["1", "China (CHN)", "14", "14", "13", "41"], ["14", "Uzbekistan (UZB)", "0", "1", "3", "4"], ["10", "Thailand (THA)", "1", "1", "2", "4"], ["12", "North Korea (PRK)", "1", "0", "0", "1"], ["6", "Japan (JPN)", "2", "13", "8", "23"], ["4", "Kazakhstan (KAZ)", "3", "4", "5", "12"], ["2", "India (IND)", "7", "6", "4", "17"], ["7", "Bahrain (BRN)", "2", "1", "1", "4"], ["15", "Iran (IRI)", "0", "1", "0", "1"], ["Total", "Total", "45", "49", "42", "136"]] | 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 countries received at least 1 bronze medal? | 10 | 128 | Answer: |
Table InputTable: [["Value", "Diameter", "Composition", "1975–1979\\nObverse", "1975–1979\\nReverse", "1981-\\nObverse", "1981-\\nReverse"], ["10 seniti", "24 mm", "Cupronickel", "King", "Grazing cattle", "King", "Bananas on tree"], ["50 seniti", "32–33 mm", "Cupronickel", "King", "Fishes around a vortex", "King", "Tomatoes"], ["20 seniti", "29 mm", "Cupronickel", "King", "Bees and hive", "King", "Yams"], ["2 seniti", "21 mm", "Bronze", "Marrows", "PLANNED FAMILIES FOOD FOR ALL, six people holding hands", "Taro", "PLANNED FAMILIES FOOD FOR ALL, six people holding hands"], ["1 seniti", "18 mm", "Bronze", "Maize", "Pig", "Maize", "Vanilla"], ["5 seniti", "19 mm", "Cupronickel", "Chicken with chicks", "Bananas", "Chicken with chicks", "Coconuts"]] | 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:adding all coins with "king" on the obverse will total how many seniti? | 80 seniti | 128 | Answer: |
Table InputTable: [["Year", "Team Record\\nW", "Team Record\\nL", "Playoffs"], ["2009", "5", "5", "Did Not Make Playoffs"], ["2005", "3", "5", "Did Not Make Playoffs"], ["2010", "5", "5", "Did Not Make Playoffs"], ["2003", "8", "1", "2nd Qualifier, Region 2"], ["2004", "7", "2", "2nd Qualifier, Region 2"], ["2001", "7", "2", "3rd Qualifier, Region 2"], ["2002", "8", "1", "2nd Qualifier, Region 2"], ["2008", "7", "4", "2nd Qualifier, Region 2"], ["2007", "13", "1", "1st Qualifier, Region 2"], ["2006", "5", "4", "4th Qualifier, Region 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:how many years did they make the playoffs? | 7 | 128 | Answer: |
Table InputTable: [["Outcome", "No.", "Date", "Tournament", "Surface", "Opponent", "Score"], ["Runner-up", "1.", "25 October 2009", "Kremlin Cup, Russia", "Hard (i)", "Mikhail Youzhny", "7–6(7–5), 0–6, 4–6"], ["Winner", "2.", "23 October 2011", "Kremlin Cup, Russia", "Hard (i)", "Viktor Troicki", "6–4, 6–2"], ["Runner-up", "4.", "18 June 2011", "Aegon International, United Kingdom", "Grass", "Andreas Seppi", "6–7(5–7), 6–3, 3–5 ret."], ["Runner-up", "5.", "30 October 2011", "St. Petersburg Open, Russia", "Hard (i)", "Marin Čilić", "3–6, 6–3, 2–6"], ["Runner-up", "2.", "19 June 2010", "UNICEF Open, Netherlands", "Grass", "Sergiy Stakhovsky", "3–6, 0–6"], ["Winner", "3.", "15 July 2012", "Stuttgart Open, Germany", "Clay", "Juan Mónaco", "6–4, 5–7, 6–3"], ["Runner-up", "7.", "22 July 2012", "Swiss Open, Switzerland", "Clay", "Thomaz Bellucci", "7–6(8–6), 4–6, 2–6"], ["Runner-up", "6.", "8 January 2012", "Chennai Open, India", "Hard", "Milos Raonic", "7–6(7–4), 6–7(4–7), 6–7(4–7)"], ["Runner-up", "3.", "27 February 2011", "International Tennis Championships, United States", "Hard", "Juan Martín del Potro", "4–6, 4–6"], ["Winner", "4.", "6 January 2013", "Chennai Open, India", "Hard", "Roberto Bautista-Agut", "3–6, 6–1, 6–3"], ["Winner", "1.", "2 October 2011", "Malaysian Open, Malaysia", "Hard (i)", "Marcos Baghdatis", "6–4, 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:how many championship games occurred in russia? | 3 | 128 | Answer: |
Table InputTable: [["State\\n(linked to\\nsummaries below)", "Incumbent\\nSenator", "Incumbent\\nParty", "Incumbent\\nElectoral\\nhistory", "Most recent election results", "2018 intent", "Candidates"], ["North Dakota", "Heidi Heitkamp", "Democratic", "Heidi Heitkamp (D) 50.2%\\nRick Berg (R) 49.3%", "2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Tennessee", "Bob Corker", "Republican", "Bob Corker (R) 64.9%\\nMark E. Clayton (D) 30.4%\\nMartin Pleasant (G) 1.7%", "2006\\n2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Ohio", "Sherrod Brown", "Democratic", "Sherrod Brown (D) 50.7%\\nJosh Mandel (R) 44.7%\\nScott A. Rupert (I) 4.6%", "2006\\n2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Massachusetts", "Elizabeth Warren", "Democratic", "Elizabeth Warren (D) 53.7%\\nScott Brown (R) 46.3%", "2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Washington", "Maria Cantwell", "Democratic", "Maria Cantwell (D) 60.5%\\nMichael Baumgartner (R) 39.5%", "2000\\n2006\\n2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Connecticut", "Chris Murphy", "Democratic", "Chris Murphy (D) 54.8%\\nLinda McMahon (R) 43.1%\\nPaul Passarelli (L) 1.7%", "2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Montana", "Jon Tester", "Democratic", "Jon Tester (D) 48.6%\\nDenny Rehberg (R) 44.9%\\nDan Cox (L) 6.6%", "2006\\n2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["California", "Dianne Feinstein", "Democratic", "Dianne Feinstein (D) 62.5%\\nElizabeth Emken (R) 37.5%", "1992 (special)\\n1994\\n2000\\n2006\\n2012", "Running", "[Data unknown/missing. You can help!]"], ["Wyoming", "John Barrasso", "Republican", "John Barrasso (R) 75.7%\\nTim Chestnut (D) 21.7%\\nJoel Otto (Wyoming Country) 2.6%", "2008 (special)\\n2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Nebraska", "Deb Fischer", "Republican", "Deb Fischer (R) 57.8%\\nBob Kerrey (D) 42.2%", "2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Virginia", "Tim Kaine", "Democratic", "Tim Kaine (D) 52.9%\\nGeorge Allen (R) 47%", "2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Hawaii", "Mazie Hirono", "Democratic", "Mazie Hirono (D) 62.6%\\nLinda Lingle (R) 37.4%", "2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["West Virginia", "Joe Manchin", "Democratic", "Joe Manchin (D) 60.6%\\nJohn Raese (R) 36.5%\\nBob Henry Baber (G) 3.0%", "2010\\n2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"]] | 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 there more male or female senators? | male | 128 | Answer: |
Table InputTable: [["Year", "Winners", "Runners-up", "Third", "Fourth"], ["1989", "Real Zaragoza", "Club de Fútbol Atlante", "Aragon", "-"], ["2001", "Real Zaragoza", "FC Twente", "-", "-"], ["1988", "Club Atlético Peñarol", "Real Zaragoza", "-", "-"], ["1995", "Real Zaragoza", "Club Nacional de Football", "-", "-"], ["2005", "Real Zaragoza", "Real Madrid Club de Fútbol", "-", "-"], ["1992", "Real Zaragoza", "Fútbol Club Barcelona", "-", "-"], ["2008", "Getafe Club de Fútbol", "Real Zaragoza", "-", "-"], ["1977", "PFC CSKA Sofia", "Real Zaragoza", "FK Radnički Niš", "RCD Espanyol"], ["1981", "Real Zaragoza", "Nottingham Forest Football Club", "Tisza Volán SC", "Club Atlético Osasuna"], ["2009", "Società Sportiva Lazio", "Real Zaragoza", "-", "-"], ["2006", "Real Zaragoza", "Associazione Sportiva Livorno Calcio", "-", "-"], ["1972", "Hamburg SV", "Sociedade Esportiva Palmeiras", "Real Zaragoza", "-"], ["1978", "Real Zaragoza", "Club Nacional de Football", "PFC Sliven", "FK Trepca Mitrovica"], ["1975", "Real Zaragoza", "Club Atlético Boca Juniors", "FK Vojvodina", "Boavista Futebol Clube"], ["2004", "Club Atlético de Madrid", "Real Zaragoza", "-", "-"], ["1997", "Società Sportiva Lazio", "Real Zaragoza", "-", "-"], ["1984", "Videoton SC", "Universidad Católica", "Real Zaragoza", "Defensor Sporting Club"], ["1985", "Fútbol Club Barcelona", "Real Zaragoza", "-", "-"], ["1993", "Club de Regatas Vasco da Gama", "Real Zaragoza", "-", "-"], ["1983", "Real Zaragoza", "Club América", "Aston Villa Football Club", "Politehnica Timişoara"], ["1979", "Real Zaragoza", "NK Dinamo Zagreb", "Vasas SC", "FK Sarajevo"], ["1974", "Real Zaragoza", "Eintracht Frankfurt", "FC Molenbeek Brussels Strombeek", "Partizan Belgrade"], ["1976", "Real Zaragoza", "Górnik Zabrze", "OFK Belgrade", "Olympiacos FC"], ["1990", "FC Dinamo Moscow", "Real Zaragoza", "Real Betis Balompié", "-"], ["2007", "Real Zaragoza", "Juventus Football Club", "-", "-"], ["1991", "Real Zaragoza", "Dinamo Bucharest", "-", "-"], ["1982", "Manchester United F.C.", "Real Zaragoza", "MTK Hungária FC", "Budapest Honvéd FC"], ["1973", "Borussia Mönchengladbach", "PFC CSKA Sofia", "Real Zaragoza", "West Ham"], ["1996", "Real Zaragoza", "Hamburg SV", "-", "-"], ["1980", "RCD Espanyol", "Real Zaragoza", "Sporting Lisboa", "Partizan Belgrade"], ["2002", "Real Zaragoza", "Athletic Club", "-", "-"], ["2003", "Real Zaragoza", "Chievo", "-", "-"], ["1971", "Cologne", "Royal Sporting Club Anderlecht", "Real Zaragoza", "-"], ["1998", "Parma", "Real Zaragoza", "-", "-"], ["1999", "Real Zaragoza", "Feyenoord Rotterdam", "-", "-"], ["2010", "Sociedad Deportiva Huesca", "Real Zaragoza", "CD Teruel", "-"], ["1994", "Real Zaragoza", "CSKA Moscow", "-", "-"], ["2000", "Real Zaragoza", "Parma", "-", "-"], ["1987", "Real Zaragoza", "Checoslovaquia", "-", "-"], ["2011", "Real Zaragoza", "RCD Espanyol", "-", "-"], ["2012", "Real Zaragoza", "RCD Espanyol", "-", "-"], ["1986", "Real Zaragoza", "Cologne", "-", "-"]] | 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 real zaragoza won? | 1974 | 128 | Answer: |
Table InputTable: [["Season", "Episodes", "Season Premiere", "Season Finale"], ["5", "40", "October 12, 2009", "June 14, 2010"], ["3", "44", "October 15, 2007", "June 2, 2008"], ["4", "48", "October 13, 2008", "May 11, 2009"], ["2", "52", "October 7, 2006", "July 16, 2007"], ["7", "8", "October 29, 2013", "December 17, 2013"], ["6", "20", "September 6, 2010", "December 6, 2010"], ["1", "20", "March 4, 2006", "May 13, 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:how many seasons had less than 40 episodes? | 3 | 128 | Answer: |
Table InputTable: [["Rank", "Island", "Area\\n(km²)", "Area\\n(sq mi)", "Country/Countries/Region"], ["230", "Austra", "88", "34", "Norway"], ["213", "Santa Maria Island", "97", "37", "Portugal"], ["273", "Graciosa Island", "62", "24", "Portugal"], ["288", "Inner-Vikna (inner island of Vikna archipelago)", "55", "21", "Norway"], ["256", "Storlandet (Finnish: Iso-Nauvo) (Nagu/Nauvo main island)", "72", "29", "Finland"], ["226", "Öja (island)", "90", "35", "Finland"], ["295", "San Pietro Island", "51", "20", "Italy"], ["243", "Ytre Vikna (outer island of Vikna archipelago)", "83", "32", "Norway"], ["283", "Kivimaa (in Gustavs/Kustavi)", "57", "22", "Finland"], ["258", "Ålön (in Pargas/Parainen)", "70", "27", "Finland"], ["221", "Gräsö", "93", "36", "Sweden"], ["298", "Sanday, Orkney", "50", "19", "United Kingdom"], ["223", "Vormsi", "92", "36", "Estonia"], ["302", "Wahlbergøya", "50", "19", "Svalbard, Norway"], ["290", "Dyrøya", "53", "20", "Norway"], ["228", "Rolvsøy, Finnmark", "89", "34", "Norway"], ["292", "Pyhämaa (in Nystad/Uusikaupunki)", "53", "20", "Finland"], ["296", "Asinara", "51", "20", "Italy"], ["269", "Ingarö", "63", "24", "Sweden"], ["245", "Rebbenesøya", "82", "32", "Norway"], ["279", "Šolta", "59", "23", "Croatia"], ["248", "Uløya", "78", "30", "Norway"], ["224", "Rab", "91", "36", "Croatia"], ["229", "Tustna", "89", "34", "Norway"], ["300", "Huftarøy", "50", "19", "Norway"], ["259", "Engeløya", "68", "26", "Norway"], ["231", "Holsnøy", "88", "34", "Norway"], ["212", "Askøy", "99", "38", "Norway"], ["276", "Leka", "60", "23", "Norway"], ["232", "Terschelling", "88", "34", "Netherlands"], ["233", "Ærø", "88", "34", "Denmark"], ["222", "Lemland", "92", "36", "Finland"], ["301", "Storøya", "50", "19", "Svalbard, Norway"], ["267", "Frei", "63", "24", "Norway"], ["272", "Raasay", "62", "24", "United Kingdom"], ["299", "Ugljan", "50", "19", "Croatia"], ["257", "Tåsinge", "70", "27", "Denmark"], ["217", "Borðoy", "95", "37", "Faroe Islands, an autonomous region of Denmark"], ["303", "South Ronaldsay", "50", "19", "United Kingdom"], ["262", "Gozo", "67", "26", "Malta"], ["238", "Nordkvaløya", "84", "33", "Norway"], ["284", "Fanø", "56", "21.5", "Denmark"], ["264", "Kyrklandet (in Korpo/Korppoo)", "64", "25", "Finland"], ["251", "Santorini", "76", "29", "Greece"], ["214", "Astypalaia", "97", "38", "Greece"], ["268", "Guernsey", "63", "24", "Guernsey, British Crown dependency"], ["297", "Hydra", "50", "19", "Greece"], ["215", "Amager", "96", "37", "Denmark"], ["218", "Salamis", "95", "37", "Greece"]] | 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 island is next in rank after austra? | Holsnøy | 128 | Answer: |
Table InputTable: [["Outcome", "No.", "Date", "Championship", "Surface", "Opponent in the final", "Score in the final"], ["Winner", "1.", "May 17, 1993", "Coral Springs, Florida, USA", "Clay", "David Wheaton", "6–3, 6–4"], ["Runner-up", "6.", "May 2, 1994", "Atlanta, Georgia, USA", "Clay", "Michael Chang", "7–6(7–4), 6–7(4–7), 0–6"], ["Runner-up", "7.", "May 9, 1994", "Pinehurst, USA", "Clay", "Jared Palmer", "4–6, 6–7(5–7)"], ["Winner", "8.", "January 18, 1999", "Sydney, Australia", "Hard", "Àlex Corretja", "6–3, 7–6(7–5)"], ["Winner", "3.", "June 13, 1994", "London (Queen's Club), UK", "Grass", "Pete Sampras", "7–6(7–4), 7–6(7–4)"], ["Runner-up", "5.", "January 31, 1994", "Australian Open, Melbourne, Australia", "Hard", "Pete Sampras", "6–7(4–7), 4–6, 4–6"], ["Winner", "5.", "January 15, 1996", "Sydney, Australia", "Hard", "Goran Ivanišević", "5–7, 6–3, 6–4"], ["Runner-up", "11.", "April 12, 1999", "Estoril, Portugal", "Clay", "Albert Costa", "6–7(4–7), 6–2, 3–6"], ["Runner-up", "10.", "August 22, 1996", "Stockholm, Sweden", "Hard (i)", "Thomas Enqvist", "5–7, 4–6, 6–7(0–7)"], ["Runner-up", "2.", "July 26, 1993", "Washington D.C., USA", "Hard", "Amos Mansdorf", "6–7(3–7), 5–7"], ["Runner-up", "12.", "September 12, 1999", "US Open, New York City, USA", "Hard", "Andre Agassi", "4–6, 7–6(7–5), 7–6(7–2), 3–6, 2–6"], ["Winner", "6.", "April 20, 1998", "Barcelona, Spain", "Clay", "Alberto Berasategui", "6–2, 1–6, 6–3, 6–2"], ["Winner", "4.", "February 20, 1995", "Memphis, Tennessee, USA", "Hard", "Paul Haarhuis", "7–6(7–2), 6–4"], ["Runner-up", "3.", "August 2, 1993", "Montreal, Canada", "Hard", "Mikael Pernfors", "6–2, 2–6, 5–7"], ["Runner-up", "9.", "February 26, 1996", "Memphis, Tennessee, USA", "Hard (i)", "Pete Sampras", "4–6, 6–7(2–7)"], ["Runner-up", "8.", "December 18, 1995", "Grand Slam Cup, Munich, Germany", "Carpet", "Goran Ivanišević", "6–7(4–7), 3–6, 4–6"], ["Winner", "7.", "November 16, 1998", "Stockholm, Sweden", "Hard", "Thomas Johansson", "6–3, 6–4, 6–4"], ["Runner-up", "4.", "October 18, 1993", "Tokyo, Japan", "Carpet", "Ivan Lendl", "4–6, 4–6"], ["Winner", "2.", "February 14, 1994", "Memphis, Tennessee, USA", "Hard", "Brad Gilbert", "6–4, 7–5"], ["Runner-up", "1.", "February 15, 1993", "Memphis, Tennessee, USA", "Hard (i)", "Jim Courier", "7–5, 6–7(4–7), 6–7(4–7)"]] | 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 was the next opponent after david wheaton? | Amos Mansdorf | 128 | Answer: |
Table InputTable: [["Outcome", "No.", "Date", "Tournament", "Surface", "Opponent", "Score"], ["Winner", "11.", "14 April 2013", "Grand Prix Hassan II, Casablanca, Morocco", "Clay", "Kevin Anderson", "7–6(8–6), 4–6, 6–3"], ["Winner", "2.", "2 May 2004", "Torneo Godó, Barcelona, Spain", "Clay", "Gastón Gaudio", "6–3, 4–6, 6–2, 3–6, 6–3"], ["Winner", "3.", "21 May 2006", "Hamburg Masters, Hamburg, Germany", "Clay", "Radek Štěpánek", "6–1, 6–3, 6–3"], ["Winner", "12.", "28 July 2013", "ATP Vegeta Croatia Open Umag, Umag, Croatia", "Clay", "Fabio Fognini", "6–0, 6–3"], ["Runner-up", "7.", "15 June 2008", "Orange Warsaw Open, Warsaw, Poland", "Clay", "Nikolay Davydenko", "3–6, 3–6"], ["Runner-up", "6.", "16 September 2007", "China Open, Beijing, China", "Hard (i)", "Fernando González", "1–6, 6–3, 1–6"], ["Winner", "7.", "13 July 2008", "Swedish Open, Båstad, Sweden (2)", "Clay", "Tomáš Berdych", "6–4, 6–1"], ["Winner", "8.", "14 February 2009", "Brasil Open, Costa do Sauípe, Brazil", "Clay", "Thomaz Bellucci", "6–3, 3–6, 6–4"], ["Winner", "5.", "5 August 2007", "Orange Warsaw Open, Sopot, Poland (2)", "Clay", "José Acasuso", "7–5, 6–0"], ["Runner-up", "2.", "20 July 2003", "Mercedes Cup, Stuttgart, Germany", "Clay", "Guillermo Coria", "2–6, 2–6, 1–6"], ["Runner-up", "4.", "30 April 2006", "Torneo Godó, Barcelona, Spain", "Clay", "Rafael Nadal", "4–6, 4–6, 0–6"], ["Winner", "6.", "7 October 2007", "Open de Moselle, Metz, France", "Hard (i)", "Andy Murray", "0–6, 6–2, 6–3"], ["Winner", "4.", "16 July 2006", "Swedish Open, Båstad, Sweden", "Clay", "Nikolay Davydenko", "6–2, 6–1"], ["Winner", "9.", "22 February 2009", "Copa Telmex, Buenos Aires, Argentina", "Clay", "Juan Mónaco", "7–5, 2–6, 7–6(7–5)"], ["Winner", "1.", "29 July 2001", "Orange Warsaw Open, Sopot, Poland", "Clay", "Albert Portas", "1–6, 7–5, 7–6(7–2)"], ["Winner", "10.", "6 February 2011", "Chile Open, Santiago, Chile", "Clay", "Santiago Giraldo", "6–2, 2–6, 7–6(7–5)"], ["Runner-up", "5.", "14 January 2007", "Heineken Open, Auckland, New Zealand", "Hard", "David Ferrer", "4–6, 2–6"], ["Runner-up", "3.", "1 May 2005", "Estoril Open, Estoril, Portugal", "Clay", "Gastón Gaudio", "1–6, 6–2, 1–6"], ["Runner-up", "1.", "15 April 2001", "Grand Prix Hassan II, Casablanca, Morocco", "Clay", "Guillermo Cañas", "5–7, 2–6"]] | 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 is the next opponent after winner number 8? | Juan Monaco | 128 | Answer: |
Table InputTable: [["Series", "Years", "Volumes", "Writer", "Editor", "Remarks"], ["Ikar", "1995–1997", "2", "Pierre Makyo", "Glénat", ""], ["Shelena", "2005", "1", "Jéromine Pasteur", "Casterman", ""], ["Jacques Le Gall", "1984–1985", "2", "Jean-Michel Charlier", "Dupuis", "A collaboration with MiTacq"], ["Daddy", "1991-92", "2", "Loup Durand", "Cl. Lefrancq", ""], ["Ivan Zourine", "1979", "2", "Jacques Stoquart", "Magic-Strip", ""], ["Les zingari", "2004–2005", "2", "Yvan Delporte", "Hibou", ""], ["L'Iliade", "1982", "1", "Jacques Stoquart", "Glénat", "Adapted from the Ilias by Homer"], ["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"], ["Alain Brisant", "1985", "1", "Maurice Tillieux", "Dupuis", ""], ["L'affaire Dominici", "2010", "1", "Pascal Bresson", "Glénat", ""], ["Terreur", "2002–2004", "2", "André-Paul Duchâteau", "Le Lombard", "Fictional biography of Madame Tussaud"], ["Valhardi", "1984–1986", "2", "Jacques Stoquart and André-Paul Duchâteau", "Dupuis", "Continuation of the series after Jijé and Eddy Paape"], ["Les autos de l'aventure", "1996–1998", "2", "De la Royère", "Citroën", "Promotional comics"], ["Bruno Brazil", "1973–1977", "5", "Greg", "Magic-Strip", "William Vance drew the comics, Follet provided the page lay-out"], ["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"], ["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)"], ["Edmund Bell", "1987–1990", "4", "Jacques Stoquart and Martin Lodewijk", "Cl. Lefrancq", "Based on the stories by John Flanders (Jean Ray)"]] | 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 editor for ikar? | Glénat | 128 | Answer: |
Table InputTable: [["Number", "Name", "Service", "From", "To"], ["(Acting)", "Maj Gen Winston P. Wilson", "USAF", "June 1, 1959", "July 19, 1959"], ["13", "MG Kenneth F. Cramer", "USA", "September 30, 1947", "September 4, 1950"], ["15", "MG Edgar C. Erickson", "USA", "June 22, 1953", "May 31, 1959"], ["16", "MG Donald W. McGowan", "USA", "July 20, 1959", "August 30, 1963"], ["12", "MG Butler B. Miltonberger", "USA", "February 1, 1946", "September 29, 1947"], ["(Acting)", "COL Ernest R. Redmond", "USA", "June 29, 1929", "September 30, 1929"], ["3", "MG Albert L. Mills", "USA", "September 1, 1912", "September 18, 1916"], ["(Acting)", "COL John F. Williams", "USA", "January 17, 1936", "January 30, 1936"], ["5", "MG Jesse McI. Carter", "USA", "February 5, 1919", "June 28, 1921"], ["(Acting)", "MG Raymond H. Fleming", "USA", "September 5, 1950", "August 13, 1951"], ["(Acting)", "MG John F. Williams", "USA", "January 31, 1944", "January 31, 1946"], ["17", "Maj Gen Winston P. Wilson", "USAF", "August 31, 1963", "August 31, 1971"], ["10", "MG Albert H. Blanding", "USA", "January 31, 1936", "January 30, 1940"], ["(Acting)", "COL George W. McIver", "USA", "September 18, 1916", "October 26, 1916"], ["(Acting)", "BG John W. Heavey", "USA", "August 15, 1918", "February 5, 1919"], ["9", "MG George E. Leach", "USA", "December 1, 1931", "November 30, 1935"], ["7", "MG Creed C. Hammond", "USA", "June 29, 1925", "June 28, 1929"], ["(Acting)", "Maj Gen Earl T. Ricks", "USAF", "February 16, 1953", "June 21, 1953"], ["5", "MG Jesse McI. Carter", "USA", "November 26, 1917", "August 15, 1918"], ["11", "MG John F. Williams", "USA", "January 31, 1940", "January 30, 1944"], ["21", "LTG Herbert R. Temple, Jr.", "USA", "August 16, 1986", "January 31, 1990"], ["22", "Lt Gen John B. Conaway", "USAF", "February 1, 1990", "December 1, 1993"], ["18", "MG Francis S. Greenlief", "USA", "September 1, 1971", "June 23, 1974"], ["8", "MG William G. Everson", "USA", "October 1, 1929", "November 30, 1931"], ["14", "MG Raymond H. Fleming", "USA", "August 14, 1951", "February 15, 1953"], ["4", "MG William A. Mann", "USA", "October 26, 1916", "November 26, 1917"], ["1", "COL Erasmus M. Weaver, Jr.", "USA", "February 14, 1908", "March 14, 1911"], ["(Acting)", "Maj Gen Philip G. Killey", "USAF", "December 2, 1993", "January 1, 1994"], ["(Acting)", "MG Raymond F. Rees", "USA", "January 2, 1994", "July 31, 1994"], ["27", "GEN Frank J. Grass", "USA", "September 7, 2012", "Present"], ["26", "Gen Craig R. McKinley", "USAF", "November 17, 2008", "September 6, 2012"]] | 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 the only (acting) chief listed in 1959 | Maj Gen Winston P. Wilson | 128 | Answer: |
Table InputTable: [["Tablet", "Genealogy", "Narrative", "Colophon"], ["10", "Descendants of Esau 36:2 - 5", "36:6 - 8", "\"This is the account of Esau.\" 36:9"], ["5", "Descendants of Shem, Ham, and Japeth 10:1 - 32", "11:1 - 9", "\"This is the account of Shem.\" 11:10"], ["9", "Abraham to Isaac 25:19", "25:20 to 35:29", "\"This is the account of Esau.\" 36:1 (eldest son)"], ["4", "Noah to Shem, Ham, and Japeth 6:9 - 10", "6:11 to 9:29", "\"This is the account of Shem, Ham, and Japheth, Noah's sons.\" 10:1"], ["7", "Terah to Abraham 11:27", "11:28 to 25:11", "\"This is the account of Abraham's son Ishmael.\" 25:12 (eldest son)"], ["3", "Adam to Noah 5:1 - 32", "6:1 - 8", "\"This is the account of Noah.\" 6:9"], ["11", "Descendants of Esau 36:10 to 37:1", "no narrative", "\"This is the account of Jacob.\" 37:2"], ["6", "Shem to Terah 11:10 - 26", "no narrative", "\"This is the account of Terah.\" 11:27"], ["2", "Heavens and Earth 2:4", "2:5 to 4:26", "\"This is the written account of Adam.\" 5:1"], ["8", "Descendants of Ishmael 25:13 - 18", "no narrative", "\"This is the account of Abraham's son Isaac.\" 25:19"], ["1", "Creation of Universe 1:1", "1:2 to 2:3", "\"This is the account of the heavens and of the earth when they were created.\" 2:4"], ["", "no genealogy", "37:2 to 50:26", "no colophon"]] | 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:tablets 9 and 10 are each accounted by who? | Esau | 128 | Answer: |
Table InputTable: [["Year", "Award", "Category", "Recipient", "Result"], ["2012", "Soompi Gayo Awards", "Top 50 Songs (#3)", "\"Heaven\"", "Won"], ["2012", "So-Loved Awards", "Best Female Newcomer", "Herself", "Won"], ["2012", "Cyworld Digital Music Awards", "Rookie & Song of the Month (February)", "\"Heaven\"", "Won"], ["2014", "Soompi Music Awards", "Best Female Artist", "\"U&I\"", "Won"], ["2013", "15th Mnet Asian Music Awards", "BC - UnionPay Song of the year", "\"U&I\"", "Nominated"], ["2013", "15th Mnet Asian Music Awards", "Best Vocal Performance - Female", "\"U&I\"", "Won"], ["2014", "28th Golden Disk Awards", "Digital Bonsang", "\"U&I\"", "Won"], ["2013", "15th Mnet Asian Music Awards", "Best Female Artist", "Herself", "Nominated"], ["2012", "Asia Song Festival", "New Artist Award", "Herself", "Won"], ["2013", "27th Golden Disk Awards", "Best New Artist", "Herself", "Won"], ["2012", "14th Mnet Asian Music Awards", "Best New Female Artist", "Herself", "Won"], ["2012", "4th MelOn Music Awards", "Best New Artist", "Herself", "Won"], ["2013", "15th Mnet Asian Music Awards", "Artist of the Year", "Herself", "Nominated"], ["2013", "Mnet Pre-Grammy Awards", "Mnet Rising Star", "Herself", "Won"], ["2013", "5th MelOn Music Awards", "Top 10 Artists", "Herself", "Won"], ["2013", "23rd Seoul Music Awards", "Rookie Award", "Herself", "Won"], ["2013", "2nd Gaon Chart K-Pop Awards", "New Female Solo Artist", "Herself", "Won"]] | 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 times the word "best" appears in the category column? | 7 | 128 | Answer: |
Table InputTable: [["Year", "Competition", "Venue", "Position", "Event"], ["2003", "World Athletics Final", "Monaco", "6th", "100 m hurdles"], ["1997", "World Indoor Championships", "Paris, France", "5th", "60 m hurdles"], ["1999", "World Indoor Championships", "Maebashi, Japan", "6th", "60 m hurdles"], ["2000", "Olympic Games", "Sydney, Australia", "3rd", "100 m hurdles"], ["2003", "World Indoor Championships", "Birmingham, England", "3rd", "60 m hurdles"], ["2000", "Grand Prix Final", "Doha, Qatar", "4th", "100 m hurdles"], ["1997", "USA Outdoor Championships", "Indianapolis, United States", "1st", "100 m hurdles"], ["2004", "Olympic Games", "Athens, Greece", "3rd", "100 m hurdles"], ["2002", "Grand Prix Final", "Paris, France", "7th", "100 m hurdles"], ["1998", "USA Indoor Championships", "", "1st", "60 m hurdles"], ["2002", "USA Indoor Championships", "", "1st", "60 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:what other competition did melissa morrison-howard place 6th besides world athletics final? | World Indoor Championships | 128 | Answer: |
Table InputTable: [["Location", "Reactor type", "Status", "Net\\nCapacity\\n(MWe)", "Gross\\nCapacity\\n(MWe)"], ["Chernobyl-2", "RBMK-1000", "shut down in 1991", "925", "1,000"], ["Chernobyl-1", "RBMK-1000", "shut down in 1996", "740", "800"], ["Chernobyl-3", "RBMK-1000", "shut down in 2000", "925", "1,000"], ["Chernobyl-4", "RBMK-1000", "destroyed in the 1986 accident", "925", "1,000"], ["Chernobyl-6", "RBMK-1000", "construction cancelled in 1988", "950", "1,000"], ["Chernobyl-5", "RBMK-1000", "construction cancelled in 1988", "950", "1,000"], ["Leningrad-2", "RBMK-1000", "operational until 2021", "925", "1,000"], ["Kursk-2", "RBMK-1000", "operational until 2024", "925", "1,000"], ["Ignalina-2", "RBMK-1500", "shut down in 2009", "1,185", "1,300"], ["Leningrad-1", "RBMK-1000", "operational", "925", "1,000"], ["Kostroma-2", "RBMK-1500", "construction cancelled in 1980s", "1,380", "1,500"], ["Smolensk-2", "RBMK-1000", "operational until July 2015", "925", "1,000"], ["Smolensk-1", "RBMK-1000", "operational until December 2022", "925", "1,000"], ["Ignalina-1", "RBMK-1500", "shut down in 2004", "1,185", "1,300"], ["Leningrad-3", "RBMK-1000", "operational until June 2025", "925", "1,000"], ["Kursk-6", "RBMK-1000", "construction cancelled in 1993", "925", "1,000"], ["Kursk-3", "RBMK-1000", "operational until March 2014", "925", "1,000"], ["Kostroma-1", "RBMK-1500", "construction cancelled in 1980s", "1,380", "1,500"], ["Kursk-1", "RBMK-1000", "operational until 2021", "925", "1,000"], ["Smolensk-4", "RBMK-1000", "construction cancelled in 1993", "925", "1,000"], ["Ignalina-3", "RBMK-1500", "construction cancelled in 1988", "1,380", "1,500"], ["Ignalina-4", "RBMK-1500", "plan cancelled in 1988", "1,380", "1,500"], ["Leningrad-4", "RBMK-1000", "operational until August 2026", "925", "1,000"], ["Kursk-4", "RBMK-1000", "operational until February 2016", "925", "1,000"], ["Smolensk-3", "RBMK-1000", "operational until July 2023", "925", "1,000"], ["Kursk-5", "MKER-1000", "construction begin was 1985, since then shelved", "925", "1,000"]] | 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:what is the difference in net capacity between chernobyl - 1 and chernobyl - 2? | 185 | 128 | Answer: |
Table InputTable: [["Rank", "City", "Passengers", "Ranking", "Airline"], ["1", "Quintana Roo, Cancún", "132,046", "", "Aeroméxico Connect, Interjet, Volaris"], ["3", "Guerrero, Acapulco", "56,069", "", "Aeroméxico Connect, Interjet"], ["8", "Baja California, Tijuana", "14,906", "", "Interjet"], ["7", "Guerrero, Ixtapa/Zihuatanejo", "35,507", "", "Interjet"], ["2", "Nuevo León, Monterrey", "106,513", "", "Aeroméxico Connect, Interjet"], ["6", "Baja California Sur, Los Cabos", "37,526", "1", "Interjet"], ["4", "Jalisco, Guadalajara", "52,584", "", "Aeroméxico Connect, Volaris"], ["5", "Jalisco, Puerto Vallarta", "43,419", "1", "Interjet"], ["10", "Tamaulipas, Tampico", "3,619", "1", "VivaAerobus"], ["9", "Tabasco, Villahermosa", "6,928", "1", "VivaAerobus"]] | 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 passengers flew to quintana roo, cancun? | 132,046 | 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"], ["2", "Jeff Lastennet", "France", "1:46.70", "", "11"], ["11", "António Rodrigues", "Portugal", "1:50.45", "", "2"], ["10", "Antonio Manuel Reina", "Spain", "1:48.56", "", "3"], ["1", "Adam Kszczot", "Poland", "1:46.50", "", "12"], ["3", "Gareth Warburton", "Great Britain", "1:46.95", "SB", "10"], ["8", "Robin Schembera", "Germany", "1:47.79", "", "5"], ["9", "Ivan Tukhtachev", "Russia", "1:48.27", "SB", "4"], ["5", "Anis Ananenka", "Belarus", "1:47.29", "", "8"], ["7", "Joni Jaako", "Sweden", "1:47.61", "SB", "6"], ["6", "Oleh Kayafa", "Ukraine", "1:47.42", "", "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:which country's athlete finished higher: france or italy? | France | 128 | Answer: |
Table InputTable: [["Pos", "No", "Driver", "Team", "Laps", "Time/Retired", "Grid", "Points"], ["13", "19", "Joël Camathias", "Dale Coyne Racing", "85", "+ 2 Laps", "18", "0"], ["14", "33", "Alex Tagliani", "Rocketsports Racing", "85", "+ 2 Laps", "14", "0"], ["2", "1", "Bruno Junqueira", "Newman/Haas Racing", "87", "+0.8 secs", "2", "17"], ["11", "27", "Bryan Herta", "PK Racing", "86", "+ 1 Lap", "12", "2"], ["15", "4", "Roberto Moreno", "Herdez Competition", "85", "+ 2 Laps", "9", "0"], ["9", "7", "Tiago Monteiro", "Fittipaldi-Dingman Racing", "86", "+ 1 Lap", "15", "4"], ["16", "11", "Geoff Boss", "Dale Coyne Racing", "83", "Mechanical", "19", "0"], ["12", "31", "Ryan Hunter-Reay", "American Spirit Team Johansson", "86", "+ 1 Lap", "17", "1"], ["17", "2", "Sébastien Bourdais", "Newman/Haas Racing", "77", "Mechanical", "4", "0"], ["7", "51", "Adrian Fernández", "Fernández Racing", "87", "+1:01.4", "5", "6"], ["18", "15", "Darren Manning", "Walker Racing", "12", "Mechanical", "7", "0"], ["5", "34", "Mario Haberfeld", "Mi-Jack Conquest Racing", "87", "+42.1 secs", "6", "10"], ["1", "32", "Patrick Carpentier", "Team Player's", "87", "1:48:11.023", "1", "22"], ["6", "20", "Oriol Servià", "Patrick Racing", "87", "+1:00.2", "10", "8"], ["8", "12", "Jimmy Vasser", "American Spirit Team Johansson", "87", "+1:01.8", "8", "5"], ["3", "3", "Paul Tracy", "Team Player's", "87", "+28.6 secs", "3", "14"], ["19", "5", "Rodolfo Lavín", "Walker Racing", "10", "Mechanical", "16", "0"], ["4", "9", "Michel Jourdain, Jr.", "Team Rahal", "87", "+40.8 secs", "13", "12"], ["10", "55", "Mario Domínguez", "Herdez Competition", "86", "+ 1 Lap", "11", "3"]] | 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 teams completed 87 laps? | 8 | 128 | Answer: |
Table InputTable: [["Series", "Name", "Age", "Hometown", "Occupation", "Status"], ["UBB", "Nikki Grahame", "28", "London", "Participated in BB7", "2nd - Runner-up"], ["BB8", "Amanda Marchant", "18", "Stoke-on-Trent", "Student", "2nd - Runner-up"], ["BBP", "Jade Goody", "23", "London", "Participated in BB3", "Not competing"], ["BB2", "Helen Adams", "22", "South Wales", "Hairdresser", "2nd - Runner-up"], ["BBP", "Nick Bateman", "37", "Kent", "Participated in BB1", "Not competing"], ["BBP", "Narinder Kaur", "23", "Leicester", "Participated in BB2", "Not competing"], ["BB13", "Adam Kelly", "27", "Dudley", "Unemployed", "2nd - Runner-up"], ["BBP", "Mel (Melanie) Hill", "30", "London", "Participated in BB1", "Not competing"], ["UBB", "Nick Bateman", "42", "Kent", "Participated in BB1", "5th - Evicted"], ["BB8", "Sam Marchant", "18", "Stoke-on-Trent", "Student", "2nd - Runner-up"], ["BB14", "Gina Rio", "24", "London", "Socialite", "3rd - Third Place"], ["UBB", "Vanessa Feltz", "48", "London", "Participated in CBB1", "8th - Evicted"], ["UBB", "Michelle Bass", "29", "Newcastle", "Participated in BB5", "9th - Evicted"], ["BB:CH", "Emilia Arata", "18", "Birmingham", "Circus Performer", "2nd - Runner-up"], ["BBP", "Spencer Smith", "25", "Cambridge", "Participated in BB3", "Not competing"], ["TBB", "Caroline Cloke", "18", "Kent", "Student", "2nd - Runner-up"], ["BB6", "Eugene Sully", "27", "Crawley", "Student", "2nd - Runner-up"], ["BB9", "Rachel Rice", "24", "Torfaen", "Trainee Teacher/Actress", "1st - Winner"], ["BB14", "Dexter Koh", "28", "London", "Celebrity publicist", "2nd - Runner-up"], ["BB1", "Anna Nolan", "29", "Dublin", "Office Manager", "2nd - Runner-up"], ["BB7", "Glyn Wise", "18", "North Wales", "Student/Lifeguard", "2nd - Runner-up"], ["BBP", "Anouska Golebiewski", "22", "Manchester", "Participated in BB4", "Not competing"], ["BB2", "Amma Antwi-Agyei", "23", "London", "Table Dancer", "7th - Evicted"], ["BBP", "Tim Culley", "22", "Worcester", "Participated in BB3", "Not competing"], ["BB7", "Nikki Grahame", "24", "London", "Model/Dancer", "5th - Evicted"], ["UBB", "Nadia Almada", "33", "London", "Participated in BB5", "10th - Evicted"], ["BB2", "Josh Rafter", "32", "London", "Property manager", "6th - Evicted"], ["BB6", "Anthony Hutton", "23", "Newcastle", "70s Dancer", "1st - Winner"], ["BB9", "Sara Folino", "27", "London", "Personal Assistant", "3rd - Third Place"], ["BB7", "Aisleyne Horgan-Wallace", "27", "London", "Model/Promotions Girl", "3rd - Third Place"], ["BB14", "Callum Knell", "28", "Kent", "Sports coach", "8th - Evicted"], ["BB5", "Vanessa Nimmo", "26", "Leeds", "Archery Champion", "11th - Evicted"], ["BB12", "Louise Cliffe", "25", "Manchester", "Model/Actress", "4th - Evicted"], ["BB9", "Mikey (Michael) Hughes", "33", "Glasgow", "Radio Producer", "2nd - Runner-up"], ["BBP", "Victor Ebuwa", "23", "London", "Participated in BB5", "Not competing"]] | 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 contestants participated in season two? | 11 | 128 | Answer: |
Table InputTable: [["Film", "Film", "Date"], ["Kodachrome 40 film", "Sound Movie film, S-8, Type A", "1974–1998"], ["Kodachrome 40 film", "Movie film, 16 mm, Type A", "1974–2006"], ["Kodachrome 40 film", "Movie film, 8 mm, Type A", "1974–1992"], ["Kodachrome 25 film", "Movie film, 8 mm, daylight", "1974–1992"], ["Kodachrome-X film", "110 format", "1972–1974"], ["Kodachrome 25 film", "Movie film, 16 mm, daylight", "1974–2002"], ["Kodachrome 40 film", "Movie film, S-8, Type A", "1974–2005"], ["Kodachrome Professional film (sheets)", "daylight (ASA 8) and Type B (ASA 10)", "1938–1951"], ["Kodachrome-X film", "35 mm (ASA 64)", "1962–1974"], ["Kodachrome-X film", "126 format", "1963–1974"], ["Kodak Color Print Material", "Type D (slide duping film)", "1955–1957"], ["Kodachrome Professional film", "35 mm, Type A (ASA 16)", "1956–1962"], ["Kodachrome film", "16 mm, daylight (ASA 10) & Type A (ASA 16)", "1935–1962"], ["Kodachrome 64", "Professional film, daylight, 120 format", "1986–1996"], ["Kodachrome II film", "Professional, 35 mm, Type A (ASA 40)", "1962–1978"], ["Kodachrome 25 film", "35 mm, daylight", "1974–2001"], ["Kodachrome film", "8 mm, daylight (ASA 10) & Type A (ASA 16)", "1936–1962"], ["Kodachrome film", "35 mm and 828, daylight & Type A", "1936–1962"], ["Kodachrome film", "35 mm and 828, Type F (ASA 12)", "1955–1962"], ["Kodachrome 25 film", "Professional film, 35 mm, daylight", "1983–1999"], ["Kodachrome 40 film", "35 mm, Type A", "1978–1997"], ["Kodachrome 64", "Professional film, 35 mm, daylight", "1983–2009"], ["Kodachrome II film", "16 mm, daylight (ASA 25) and Type A (ASA 40)", "1961–1974"], ["Kodachrome 200", "Professional film, 35 mm, daylight", "1986–2004"], ["Kodachrome II film", "8 mm, daylight (ASA 25) and Type A (ASA 40)", "1961–1974"], ["Kodachrome II film", "S-8, Type A (ASA 40)", "1965–1974"], ["Kodachrome II film", "35 mm and 828, daylight (ASA 25/early) (ASA 64/late)", "1961–1974"], ["Kodachrome 64", "110 format, daylight", "1974–1987"], ["Kodachrome 64", "35 mm, daylight", "1974–2009"], ["Kodachrome 64", "126 format, daylight", "1974–1993"], ["Kodachrome 200", "35 mm, daylight", "1988–2007"], ["Cine-Chrome 40A", "Double Regular 8 mm, tungsten", "2003–2006"]] | 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:if you wanted to make a sound movie which film would you chose to use? | Kodachrome 40 film | 128 | Answer: |
Table InputTable: [["Round", "Date", "Circuit", "Winning driver (TA2)", "Winning vehicle (TA2)", "Winning driver (TA1)", "Winning vehicle (TA1)"], ["5", "July 8", "Watkins Glen‡", "Hal Shaw, Jr.\\n Monte Shelton", "Porsche 935", "Brian Fuerstenau\\n Bob Tullius", "Jaguar XJS"], ["7", "August 19", "Mosport", "Greg Pickett", "Chevrolet Corvette", "Bob Tullius", "Jaguar XJS"], ["1", "May 21", "Sears Point", "Greg Pickett", "Chevrolet Corvette", "Gene Bothello", "Chevrolet Corvette"], ["8", "September 4", "Road America", "Greg Pickett", "Chevrolet Corvette", "Bob Tullius", "Jaguar XJS"], ["4", "June 25", "Mont-Tremblant", "Monte Sheldon", "Porsche 935", "Bob Tullius", "Jaguar XJS"], ["9", "October 8", "Laguna Seca", "Greg Pickett", "Chevrolet Corvette", "Bob Tullius", "Jaguar XJS"], ["3", "June 11", "Portland", "Tuck Thomas", "Chevrolet Monza", "Bob Matkowitch", "Chevrolet Corvette"], ["10", "November 5", "Mexico City", "Ludwig Heimrath", "Porsche 935", "Bob Tullius", "Jaguar XJS"], ["2", "June 4", "Westwood", "Ludwig Heimrath", "Porsche 935", "Nick Engels", "Chevrolet Corvette"], ["6", "August 13", "Brainerd", "Jerry Hansen", "Chevrolet Monza", "Bob Tullius", "Jaguar XJS"]] | 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 date listed for the last round? | November 5 | 128 | Answer: |
Table InputTable: [["Team", "Chassis", "Engine", "Tires", "No", "Drivers"], ["All American Racing", "Reynard 98i\\nEagle 987", "Toyota", "Goodyear", "98", "P. J. Jones\\n Vincenzo Sospiri"], ["Newman-Haas Racing", "Swift 009.c", "Ford XB", "Goodyear", "6", "Michael Andretti"], ["Newman-Haas Racing", "Swift 009.c", "Ford XB", "Goodyear", "11", "Christian Fittipaldi\\n Roberto Moreno"], ["All American Racing", "Reynard 98i\\nEagle 987", "Toyota", "Goodyear", "36", "Alex Barron"], ["Arciero-Wells Racing", "Reynard 98i", "Toyota", "Firestone", "24", "Hiro Matsushita\\n Robby Gordon"], ["Payton/Coyne Racing", "Reynard 98i", "Ford XB", "Firestone", "19", "Michel Jourdain, Jr."], ["Chip Ganassi Racing", "Reynard 98i", "Honda", "Firestone", "1", "Alex Zanardi"], ["Chip Ganassi Racing", "Reynard 98i", "Honda", "Firestone", "12", "Jimmy Vasser"], ["Payton/Coyne Racing", "Reynard 98i", "Ford XB", "Firestone", "34", "Dennis Vitolo\\n Gualter Salles"], ["Walker Racing", "Reynard 98i", "Honda", "Goodyear", "5", "Gil de Ferran"], ["PacWest Racing Group", "Reynard 98i", "Mercedes", "Firestone", "17", "Maurício Gugelmin"], ["PacWest Racing Group", "Reynard 98i", "Mercedes", "Firestone", "18", "Mark Blundell"], ["Hogan Racing", "Reynard 98i", "Mercedes", "Firestone", "9", "JJ Lehto"], ["Arciero-Wells Racing", "Reynard 98i", "Toyota", "Firestone", "25", "Max Papis"], ["Davis Racing", "Lola T98/00", "Ford XB", "Goodyear", "77", "Arnd Meier"], ["Bettenhausen Racing", "Reynard 98i", "Mercedes", "Goodyear", "16", "Hélio Castroneves"], ["Patrick Racing", "Reynard 98i", "Ford XB", "Firestone", "20", "Scott Pruett"], ["Forsythe Racing", "Reynard 98i", "Mercedes", "Firestone", "99", "Greg Moore"], ["Patrick Racing", "Reynard 98i", "Ford XB", "Firestone", "40", "Adrián Fernández"], ["Marlboro Team Penske", "Penske PC27-98", "Mercedes", "Goodyear", "3", "André Ribeiro"], ["Forsythe Racing", "Reynard 98i", "Mercedes", "Firestone", "33", "Patrick Carpentier"], ["Della Penna Motorsports", "Swift 009.c", "Ford XB", "Firestone", "43", "Hideshi Matsuda"], ["Marlboro Team Penske", "Penske PC27-98", "Mercedes", "Goodyear", "2", "Al Unser, Jr."], ["Della Penna Motorsports", "Swift 009.c", "Ford XB", "Firestone", "10", "Richie Hearn"], ["Tasman Motorsports Group", "Reynard 98i", "Honda", "Firestone", "21", "Tony Kanaan"], ["Team KOOL Green", "Reynard 98i", "Honda", "Firestone", "26", "Paul Tracy"], ["Project Indy", "Reynard 97i", "Ford XB", "Goodyear", "15", "Roberto Moreno\\n Domenico Schiattarella"], ["Team Rahal", "Reynard 98i", "Ford XB", "Firestone", "8", "Bryan Herta"], ["Team KOOL Green", "Reynard 98i", "Honda", "Firestone", "27", "Dario Franchitti"], ["Team Rahal", "Reynard 98i", "Ford XB", "Firestone", "7", "Bobby Rahal"]] | 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 u.s. drivers raced? | 11 | 128 | Answer: |
Table InputTable: [["School", "2007", "2008", "2009", "2010", "2011"], ["James A. Garfield High School", "553", "597", "593", "632", "705"], ["Thomas Jefferson High School", "457", "516", "514", "546", "546"], ["Abraham Lincoln High School", "594", "609", "588", "616", "643"], ["Marc and Eva Stern Math and Science School", "718", "792", "788", "788", "809"], ["Oscar De La Hoya Animo Charter High School", "662", "726", "709", "710", "744"], ["Theodore Roosevelt High School", "557", "551", "576", "608", ""], ["Woodrow Wilson High School", "582", "585", "600", "615", "636"], ["Francisco Bravo Medical Magnet High School", "807", "818", "815", "820", "832"], ["Santee Education Complex", "", "502", "521", "552", "565"]] | 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 difference between james a. garfield high school's 2007 score and its 2011 score? | 152 | 128 | Answer: |
Table InputTable: [["State\\n(linked to\\nsummaries below)", "Incumbent\\nSenator", "Incumbent\\nParty", "Incumbent\\nElectoral\\nhistory", "Most recent election results", "2018 intent", "Candidates"], ["Tennessee", "Bob Corker", "Republican", "Bob Corker (R) 64.9%\\nMark E. Clayton (D) 30.4%\\nMartin Pleasant (G) 1.7%", "2006\\n2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Florida", "Bill Nelson", "Democratic", "Bill Nelson (D) 55.2%\\nConnie Mack IV (R) 42.2%", "2000\\n2006\\n2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["California", "Dianne Feinstein", "Democratic", "Dianne Feinstein (D) 62.5%\\nElizabeth Emken (R) 37.5%", "1992 (special)\\n1994\\n2000\\n2006\\n2012", "Running", "[Data unknown/missing. You can help!]"], ["North Dakota", "Heidi Heitkamp", "Democratic", "Heidi Heitkamp (D) 50.2%\\nRick Berg (R) 49.3%", "2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Minnesota", "Amy Klobuchar", "Democratic", "Amy Klobuchar (D) 65.2%\\nKurt Bills (R) 30.5%\\nStephen Williams (Independence) 2.6%", "2006\\n2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Wyoming", "John Barrasso", "Republican", "John Barrasso (R) 75.7%\\nTim Chestnut (D) 21.7%\\nJoel Otto (Wyoming Country) 2.6%", "2008 (special)\\n2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Virginia", "Tim Kaine", "Democratic", "Tim Kaine (D) 52.9%\\nGeorge Allen (R) 47%", "2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Nebraska", "Deb Fischer", "Republican", "Deb Fischer (R) 57.8%\\nBob Kerrey (D) 42.2%", "2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Texas", "Ted Cruz", "Republican", "Ted Cruz (R) 56.5%\\nPaul Sadler (D) 40.7%\\nJohn Jay Myers (L) 2.1%", "2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Nevada", "Dean Heller", "Republican", "Dean Heller (R) 45.9%\\nShelley Berkley (D) 44.7%\\nDavid Lory VanderBeek (C) 4.9%\\nNone of These Candidates 4.5%", "2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Washington", "Maria Cantwell", "Democratic", "Maria Cantwell (D) 60.5%\\nMichael Baumgartner (R) 39.5%", "2000\\n2006\\n2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Connecticut", "Chris Murphy", "Democratic", "Chris Murphy (D) 54.8%\\nLinda McMahon (R) 43.1%\\nPaul Passarelli (L) 1.7%", "2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"], ["Massachusetts", "Elizabeth Warren", "Democratic", "Elizabeth Warren (D) 53.7%\\nScott Brown (R) 46.3%", "2012", "[Data unknown/missing. You can help!]", "[Data unknown/missing. You can help!]"]] | 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 senators have at least two consecutive terms? | 20 | 128 | Answer: |
Table InputTable: [["Date", "Competition", "Location", "Country", "Event", "Placing", "Rider", "Nationality"], ["1 November 2008", "2008–09 World Cup", "Manchester", "United Kingdom", "500 m time trial", "1", "Victoria Pendleton", "GBR"], ["1 November 2008", "2008–09 World Cup", "Manchester", "United Kingdom", "Sprint", "1", "Jason Kenny", "GBR"], ["31 October 2008", "2008–09 World Cup", "Manchester", "United Kingdom", "Sprint", "1", "Victoria Pendleton", "GBR"], ["13 February 2009", "2008–09 World Cup", "Copenhagen", "Denmark", "Sprint", "1", "Victoria Pendleton", "GBR"], ["2 November 2008", "2008–09 World Cup", "Manchester", "United Kingdom", "Keirin", "1", "Victoria Pendleton", "GBR"], ["31 October 2008", "2008–09 World Cup", "Manchester", "United Kingdom", "Keirin", "2", "Jason Kenny", "GBR"], ["2 November 2008", "2008–09 World Cup", "Manchester", "United Kingdom", "Team sprint", "1", "Ross Edgar", "GBR"], ["2 November 2008", "2008–09 World Cup", "Manchester", "United Kingdom", "Team sprint", "1", "Jason Kenny", "GBR"], ["30 October 2009", "2009–10 World Cup", "Manchester", "United Kingdom", "500 m time trial", "2", "Victoria Pendleton", "GBR"], ["13 February 2009", "2008–09 World Cup", "Copenhagen", "Denmark", "Team sprint", "1", "Chris Hoy", "GBR"], ["2 November 2008", "5th International Keirin Event", "Manchester", "United Kingdom", "International keirin", "2", "Ross Edgar", "GBR"], ["13 February 2009", "2008–09 World Cup", "Copenhagen", "Denmark", "Team sprint", "1", "Jason Kenny", "GBR"], ["30 October 2009", "2009–10 World Cup", "Manchester", "United Kingdom", "Sprint", "1", "Victoria Pendleton", "GBR"], ["30 October 2009", "2009–10 World Cup", "Manchester", "United Kingdom", "Sprint", "1", "Chris Hoy", "GBR"], ["2 November 2008", "2008–09 World Cup", "Manchester", "United Kingdom", "Team sprint", "1", "Jamie Staff", "GBR"], ["13 February 2009", "2008–09 World Cup", "Copenhagen", "Denmark", "Team sprint", "1", "Jamie Staff", "GBR"], ["1 November 2009", "2009–10 World Cup", "Manchester", "United Kingdom", "Team sprint", "1", "Ross Edgar", "GBR"], ["1 November 2009", "2009–10 World Cup", "Manchester", "United Kingdom", "Team sprint", "1", "Chris Hoy", "GBR"], ["30 October 2009", "2009–10 World Cup", "Manchester", "United Kingdom", "Keirin", "1", "Chris Hoy", "GBR"], ["1 November 2009", "2009–10 World Cup", "Manchester", "United Kingdom", "Team sprint", "1", "Jamie Staff", "GBR"]] | 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 last date on the list? | 1 November 2009 | 128 | Answer: |
Table InputTable: [["Goal", "Date", "Venue", "Opponent", "Score", "Result", "Competition"], ["6", "2012-2-29", "Tsirion Stadium, Limassol, Cyprus", "Canada", "1-1", "1-3", "Friendly match"], ["7", "2012-2-29", "Tsirion Stadium, Limassol, Cyprus", "Canada", "1-2", "1-3", "Friendly match"], ["1", "2008-5-28", "Sheriff Stadium, Tiraspol, Moldova", "Moldova", "0-1", "2–2", "Friendly match"], ["3", "2011-6-4", "Petrovsky Stadium, Saint Petersburg, Russia", "Russia", "0-1", "3–1", "Euro 2012 Q"], ["2", "2010-10-12", "Hanrapetakan Stadium, Yerevan, Armenia", "Andorra", "4–0", "4–0", "Euro 2012 Q"], ["5", "2011-10-7", "Hanrapetakan Stadium, Yerevan, Armenia", "Macedonia", "1-0", "4-1", "Euro 2012 Q"], ["4", "2011-9-2", "Estadi Comunal d'Aixovall, Andorra la Vella, Andorra", "Andorra", "0-1", "0-3", "Euro 2012 Q"]] | 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:against which international team did marcos pizzelli score the most goals? | Andorra | 128 | Answer: |
Table InputTable: [["Name", "Route number(s)", "Length (mi)", "Location", "Notes"], ["Henry E. Bodurtha Highway", "", "5.0", "Agawam", "Freeway comprises the eastern 5 miles (8.0 km) of Route 57, connecting Route 187 to U.S. Route 5 and I-91."], ["Grand Army of the Republic Highway", "*", "117.46", "Seekonk to Provincetown", "The cross-country U.S. Route 6 is designated Grand Army of the Republic Highway over its entire length, which spans 3,205 miles (5,158 km)."], ["Yankee Division Highway\\n(Circumferential Highway)", "*", "64.74", "Braintree to Gloucester", "The Yankee Division Highway consists of the Route 128 beltway before it was truncated to its southern terminus in Canton, and continues to span its entire length. It stretches from I-93's Exit 7 in Braintree to Route 128's northern terminus at Route 127A in Gloucester.\\n- I-95 runs along the highway between Exits 12 and 45 (concurrent with 128).\\n- I-93 runs along the highway between Exits 1 and 7.\\n- U.S. Route 1 runs along the highway between I-95 Exit 15B and I-93 Exit 7."], ["Wilbur Cross Highway", "*", "8.0", "Sturbridge", "I-84 in Massachusetts is designated the Wilbur Cross Highway. It runs 8 miles (13 km) from the Connecticut state border to the Mass Pike at Exit 9."], ["Pilgrims Highway", "", "42.5", "Bourne to Braintree", "The Pilgrims Highway is the southern portion of Route 3, a 42-mile (68 km) long freeway which serves as a connector between Cape Cod (via U.S. Route 6) and the Boston metropolitan area (via I-93 and I-95).\\n- U.S. Route 44 runs along the highway between Exits 6 and 7."], ["Mohawk Trail", "", "65", "Williamstown\\nto Orange", "The 65-mile (105 km) Mohawk Trail comprises the western section of Route 2, from the New York border east to Orange, and is regarded as one of the most scenic drives in the area."], ["Taunton-New Bedford Expressway\\n(Alfred M. Bessette Memorial Highway)", "", "19.3", "New Bedford to Taunton", "The New Bedford Expressway comprises the southern 19 miles (31 km) of Route 140, and serves as a freeway connection between U.S. Route 6 in New Bedford and Route 24 (Exit 12) in Taunton, near I-495."], ["Massachusetts Turnpike", "*", "138.1", "West Stockbridge\\nto Boston", "The Mass Pike is a toll road running from the New York state border to downtown Boston. It serves as the main cross-state freeway connecting the western and eastern portions of the state. The \"Pike\" carries the easternmost 138 miles (222 km) of cross-country Interstate 90."], ["East Boston Expressway", "", "1.2", "Boston", "The East Boston Expressway comprises the first 1.2 miles (1.9 km) of Route 1A's northern segment. It stretches from I-93 Exit 24 at the southern end of the Callahan Tunnel (northbound) and the Sumner Tunnel (southbound) to just northeast of the interchange with Route 145 in East Boston, near the eastern end of the Mass Pike."], ["Mid-Cape Highway", "", "36.6", "Bourne to Orleans", "The Mid-Cape Highway is the main highway on Cape Cod, a 36-mile (58 km) long freeway running from Route 3 and the Sagamore Bridge east to the Orleans Rotary."], ["Lydia Taft Highway", "*", "3", "Uxbridge", "Route 146A in Massachusetts is designated as the Lydia Taft Highway, which runs from the Rhode Island state border to Route 122 in Uxbridge."], ["Amvets Memorial Highway\\n(Fall River Expressway)", "*", "40.91", "Fall River to Randolph", "Route 24 is a connector between the Fall River/New Bedford area east of Rhode Island to the Boston metropolitan area, connecting the major freeways of the area: I-195 in Fall River and I-93/US-1 near I-95 in Randolph.\\n- Route 79 runs along the highway between Exits 7 and 9, concurrent with Route 24.\\n- I-195 has a brief concurrency with Route 24 in Fall River."]] | 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 is henry e. bodurtha highway? | 5.0 | 128 | Answer: |
Table InputTable: [["Season", "Tier", "Division", "Place"], ["1989/90", "4", "3ª", "1st"], ["1997/98", "4", "3ª", "1st"], ["1998/99", "4", "3ª", "6th"], ["1988/89", "4", "3ª", "3rd"], ["1994/95", "3", "2ªB", "9th"], ["1991/92", "3", "2ªB", "12th"], ["1996/97", "4", "3ª", "2nd"], ["1990/91", "3", "2ªB", "6th"], ["1993/94", "3", "2ªB", "15th"], ["1995/96", "3", "2ªB", "19th"], ["1992/93", "3", "2ªB", "4th"]] | 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 palencia place first? | 2 | 128 | Answer: |
Table InputTable: [["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"], ["2", "Japan (JPN)", "46", "56", "77", "179"], ["6", "North Korea (PRK)", "6", "10", "20", "36"], ["1", "China (CHN)", "127", "63", "33", "223"], ["3", "South Korea (KOR)", "32", "48", "65", "145"], ["9", "Guam (GUM)", "0", "0", "1", "1"], ["Total", "Total", "237", "230", "254", "721"], ["8", "Mongolia (MGL)", "1", "1", "6", "8"], ["7", "Hong Kong (HKG)", "2", "2", "9", "13"], ["4", "Chinese Taipei (TPE)", "12", "34", "26", "72"], ["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:how many nations earned zero silver medals? | 1 | 128 | Answer: |
Table InputTable: [["Outcome", "No.", "Date", "Tournament", "Surface", "Partner", "Opponents", "Score"], ["Winner", "5.", "12 November 2012", "Zawada, Poland", "Carpet (i)", "Karolína Plíšková", "Kristina Barrois\\n Sandra Klemenschits", "6–3, 6–1"], ["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", "2.", "7 August 2011", "Vancouver, Canada", "Hard", "Karolína Plíšková", "Jamie Hampton\\n N. Lertcheewakarn", "5–7, 6–2, 6–4"], ["Winner", "4.", "30 January 2012", "Grenoble, France", "Hard (i)", "Karolína Plíšková", "Valentyna Ivakhnenko\\n Maryna Zanevska", "6–1, 6–3"], ["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"], ["Runner-up", "3.", "20 November 2011", "Bratislava, Slovakia", "Hard", "Karolína Plíšková", "Naomi Broady\\n Kristina Mladenovic", "7–5, 4–6, [2–10]"], ["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"], ["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", "1.", "16 May 2010", "Kurume, Japan", "Clay", "Karolína Plíšková", "Sun Shengnan\\n Xu Yifan", "0–6, 3–6"]] | 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:what is the total number of winners? | 6 | 128 | Answer: |
Table InputTable: [["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"], ["9", "Uruguay", "0", "0", "1", "1"], ["1", "Brazil", "7", "5", "3", "15"], ["5", "Argentina", "1", "2", "5", "8"], ["9", "Netherlands Antilles", "0", "0", "1", "1"], ["7", "Ecuador", "0", "2", "2", "4"], ["9", "Panama", "0", "0", "1", "1"], ["2", "Venezuela", "3", "2", "8", "13"], ["8", "Guyana", "0", "1", "0", "1"], ["4", "Chile", "2", "0", "2", "4"], ["6", "Peru", "1", "1", "2", "4"], ["3", "Colombia", "2", "3", "4", "9"], ["9", "Aruba", "0", "0", "1", "1"], ["Total", "Total", "16", "16", "30", "62"]] | 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 won one silver medal but no bronze medals? | Guyana | 128 | Answer: |
Table InputTable: [["Year", "Award", "Category", "Recipient", "Result"], ["2012", "So-Loved Awards", "Best Female Newcomer", "Herself", "Won"], ["2012", "14th Mnet Asian Music Awards", "Best New Female Artist", "Herself", "Won"], ["2012", "4th MelOn Music Awards", "Best New Artist", "Herself", "Won"], ["2012", "Asia Song Festival", "New Artist Award", "Herself", "Won"], ["2013", "Mnet Pre-Grammy Awards", "Mnet Rising Star", "Herself", "Won"], ["2013", "15th Mnet Asian Music Awards", "Best Female Artist", "Herself", "Nominated"], ["2013", "15th Mnet Asian Music Awards", "Artist of the Year", "Herself", "Nominated"], ["2013", "5th MelOn Music Awards", "Top 10 Artists", "Herself", "Won"], ["2013", "15th Mnet Asian Music Awards", "Best Vocal Performance - Female", "\"U&I\"", "Won"], ["2012", "Cyworld Digital Music Awards", "Rookie & Song of the Month (February)", "\"Heaven\"", "Won"], ["2013", "27th Golden Disk Awards", "Best New Artist", "Herself", "Won"], ["2013", "23rd Seoul Music Awards", "Rookie Award", "Herself", "Won"], ["2012", "Soompi Gayo Awards", "Top 50 Songs (#3)", "\"Heaven\"", "Won"], ["2014", "Soompi Music Awards", "Best Female Artist", "\"U&I\"", "Won"], ["2013", "2nd Gaon Chart K-Pop Awards", "New Female Solo Artist", "Herself", "Won"], ["2013", "15th Mnet Asian Music Awards", "BC - UnionPay Song of the year", "\"U&I\"", "Nominated"], ["2014", "28th Golden Disk Awards", "Digital Bonsang", "\"U&I\"", "Won"]] | 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 awards did ailee win in 2012? | 6 | 128 | Answer: |
Table InputTable: [["Country", "Amphibians", "Birds", "Mammals", "Reptile", "Total terrestrial vertebrates", "Vascular plants", "Biodiversity"], ["Costa Rica", "183", "838", "232", "258", "1511", "12119", "13630"], ["Guatemala", "133", "684", "193", "236", "1246", "8681", "9927"], ["Belize", "46", "544", "147", "140", "877", "2894", "3771"], ["Nicaragua", "61", "632", "181", "178", "1052", "7590", "8642"], ["Panama", "182", "904", "241", "242", "1569", "9915", "11484"], ["El Salvador", "30", "434", "137", "106", "707", "2911", "3618"], ["Honduras", "101", "699", "201", "213", "1214", "5680", "6894"]] | 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 would be best to visit for someone who likes birds? | Panama | 128 | Answer: |
Table InputTable: [["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"], ["1", "Soviet Union", "*7*", "3", "6", "16"], ["9", "Germany", "1", "0", "1", "2"], ["2", "Austria", "4", "3", "4", "11"], ["4", "Switzerland", "3", "2", "1", "6"], ["7", "Norway", "2", "1", "1", "4"], ["8", "Italy", "1", "2", "0", "3"], ["3", "Finland", "3", "3", "1", "7"], ["5", "Sweden", "2", "4", "4", "10"], ["6", "United States", "2", "3", "2", "7"], ["10", "Canada", "0", "1", "2", "3"]] | 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 nation came in first with the most medals combined? | Soviet Union | 128 | Answer: |
Table InputTable: [["Year", "Total\\npassengers", "Passenger\\nChange", "Domestic", "International\\n(total)", "International\\n(non-CIS)", "CIS", "Aircraft\\nLandings", "Cargo\\n(tonnes)"], ["2004", "1 553 628", "+16,3%", "972 287", "581 341", "429 049", "152 292", "11 816", "20 457"], ["2012", "3 783 069", "+12.7%", "1 934 016", "1 849 053", "1 448 765", "439 668", "21 728", "25 866"], ["2003", "1 335 757", "+12,9%", "879 665", "456 092", "297 421", "158 671", "10 092", "18 054"], ["2006", "1 764 948", "+12,7%", "1 128 489", "636 459", "488 954", "147 505", "13 289", "15 519"], ["2008", "2 529 395", "+7,8%", "1 523 102", "1 006 293", "815 124", "191 169", "16 407", "17 142"], ["2005", "1 566 792", "+0,8%", "1 006 422", "560 370", "429 790", "130 580", "11 877", "11 545"], ["2007", "2 345 097", "+32,9%", "1 486 888", "858 209", "683 092", "175 117", "16 767", "16 965"], ["2010", "2 748 919", "+26,7%", "1 529 245", "1 219 674", "1 017 509", "202 165", "15 989", "22 946"], ["2001", "1 028 295", "+10,5%", "733 022", "295 273", "186 861", "108 412", "9 062", "22 178"], ["2002", "1 182 815", "+15,0%", "793 295", "389 520", "239 461", "150 059", "10 162", "20 153"], ["2011", "3 355 883", "+22,1%", "1 856 948", "1 498 935", "1 184 771", "314 164", "20 142", "24 890"], ["2013", "4 293 002", "+13.5%", "2 180 227", "2 112 775", "", "", "25 728", "27 800"], ["2009", "2 169 136", "−14,2%", "1 290 639", "878 497", "727 718", "150 779", "13 798", "13 585"], ["2000", "930 251", "+2%", "698 957", "231 294", "155 898", "75 396", "8 619", "18 344"]] | 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 last year in which there were fewer than one million domestic passengers? | 2004 | 128 | Answer: |
Table InputTable: [["Town name", "County", "Established", "Disestablished", "Current Status", "Remarks"], ["Tontzville", "Miami County", "1866", "1874", "Nothing remains of the townsite.", ""], ["Thurman", "Chase County", "1874", "1944", "Little remains of the townsite.", ""], ["Salem", "Jewell County", "1871", "", "Nothing remains of the townsite.", ""], ["Burntwood City", "Rawlins County", "1860s", "", "Nothing remains of the townsite.", ""], ["Minneola", "Franklin County", "1854", "1860s", "Nothing remains of the town.", "Was the territorial capitol briefly in 1858. Not to be confused with Minneola in Clark County."], ["Williamsport", "Shawnee County", "1857", "", "Nothing remains of the townsite.", ""], ["Franklin", "Douglas County", "1853 (early stage stop)", "Post office closed 1867", "Nothing remains of the town except two small neglected cemeteries and Franklin Road off of K-10.", ""], ["Ohio City", "Franklin County", "1857", "1864", "Nothing remains of the townsite.", "Was the county seat from 1861 until 1864."], ["Dermot", "Stevens County", "1887", "Post office closed in 1929", "Nothing remains of the townsite.", "The town was short-lived but the post office existed decades longer than the actual town."], ["Taloga", "Morton County", "1886", "1890s", "Nothing remains of the townsite.", ""], ["Goguac", "Stanton County", "1889", "1890s", "Nothing remains of the townsite.", ""], ["Emerald Community", "Anderson County", "1857", "", "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.", ""], ["Cash City", "Clark County", "1885", "1895", "Nothing remains of the townsite.", ""], ["Runnymede", "Harper County", "1887", "1892", "Nothing remains of the town.", ""], ["Woodsdale", "Stevens County", "1885", "late 1880s", "Nothing remains of the townsite.", "Battled with Hugoton for county seat of Stevens County."], ["Farnsworth", "Lane County", "1880", "1891", "Nothing remains.", ""], ["Leota", "Norton County", "1873", "1882", "Nothing remains of the townsite.", ""], ["Marshall", "Sedgwick County", "1872", "1880s", "Nothing remains of the townsite.", ""], ["Devizes", "Norton County", "1873", "1930s", "Nothing remains of the townsite.", ""], ["Port Landis", "Norton County", "1872", "", "Nothing remains of the townsite.", ""], ["Pearlette", "Meade County", "1879", "1880", "Nothing remains of the townsite.", ""], ["Touzalin", "Meade County", "1884", "1885", "Nothing remains of the townsite.", ""], ["Chantilly", "Kearny County", "1887", "1893", "Nothing remains of the townsite.", ""], ["Terry", "Finney County", "1885", "1890s", "Nothing remains of the townsite.", ""], ["Smoky Hill City", "Ellis County", "1899", "1905", "Nothing remains of the townsite.", ""], ["Paris", "Linn County", "1854", "", "Nothing remains of the townsite.", ""], ["Afton", "Marshall County", "1893", "", "A small cemetery and church mark the townsite.", ""], ["Colokan", "Greeley County", "1886", "1897", "Nothing remains of the townsite.", ""], ["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."], ["Borders", "Stanton County", "1887", "1888", "Nothing remains of the townsite.", ""], ["Elk", "Chase County", "", "Post office closed in 1923", "Nothing 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 total ghost towns are there in franklin county? | 3 | 128 | Answer: |
Table InputTable: [["Name", "Type", "R.A. (J2000)", "Dec. (J2000)", "Redshift (km/s)", "Apparent Magnitude"], ["UGCA 86", "Im", "03h 59m 50.5s", "+67° 08′ 37″", "67 ± 4", "13.5"], ["Camelopardalis A", "Irr", "04h 26m 16.3s", "+72° 48′ 21″", "-46 ± 1", "14.8"], ["Camelopardalis B", "Irr", "04h 53m 07.1s", "+67° 05′ 57″", "77", "16.1"], ["UGCA 92", "Im", "04h 32m 04.9s", "+63° 36′ 49.0″", "-99 ± 5", "13.8"], ["Cassiopeia 1", "dIrr", "02h 06m 02.8s", "+68° 59′ 59″", "35", "16.4"], ["UGCA 105", "Im", "05h 14m 15.3s", "+62° 34′ 48″", "111 ± 5", "13.9"], ["IC 342", "SAB(rs)cd", "03h 46m 48.5s", "+68° 05′ 46″", "31 ± 3", "9.1"], ["KK 35", "Irr", "03h 45m 12.6s", "+67° 51′ 51″", "105 ± 1", "17.2"], ["NGC 1560", "SA(s)d", "04h 32m 49.1s", "+71° 52′ 59″", "-36 ± 5", "12.2"], ["NGC 1569", "Sbrst", "04h 30m 49.1s", "+64° 50′ 52,6″", "-104 ± 4", "11,2"]] | 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:what is the total of names? | 10 | 128 | Answer: |
Table InputTable: [["Description Losses", "1939/40", "1940/41", "1941/42", "1942/43", "1943/44", "1944/45", "Total"], ["Direct War Losses", "360,000", "", "", "", "", "183,000", "543,000"], ["Total", "504,000", "352,000", "407,000", "541,000", "681,000", "270,000", "2,770,000"], ["Deaths other countries", "", "", "", "", "", "", "2,000"], ["Murdered", "75,000", "100,000", "116,000", "133,000", "82,000", "", "506,000"], ["Deaths In Prisons & Camps", "69,000", "210,000", "220,000", "266,000", "381,000", "", "1,146,000"], ["Deaths Outside of Prisons & Camps", "", "42,000", "71,000", "142,000", "218,000", "", "473,000"], ["Murdered in Eastern Regions", "", "", "", "", "", "100,000", "100,000"]] | 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:what is the last description of losses on this chart? | Deaths other countries | 128 | Answer: |
Table InputTable: [["Boat", "Time", "Crew", "Nation", "Date", "Meet", "Location"], ["LM2x\\nLightweight double sculls", "6:10.02", "Mads Rasmussen\\nRasmus Quist", "Denmark", "2007", "", "Amsterdam, Netherlands"], ["LM4x\\nLightweight quad sculls", "5:45.18", "Francesco Esposito\\nMassimo Lana\\nMichelangelo Crispi\\nMassimo Guglielmi", "Italy", "1992", "", "Montreal, Canada"], ["LM2-\\nLightweight coxless pairs", "6:26.21", "Tony O'Connor\\nNeville Maxwell", "Ireland", "1994", "", "Paris, France"], ["LM1x\\nLightweight single sculls", "6:46.93", "Jeremie Azou", "France", "2011", "", "Amsterdam, Netherlands"], ["M2x\\nDouble sculls", "6:03.25", "Adrien Hardy\\nJean-Baptiste Macquet", "France", "2006", "", "Poznań, Poland"], ["M1x\\nSingle sculls", "6:33.35", "Mahé Drysdale", "New Zealand", "2009", "", "Poznań, Poland"], ["LM4-\\nLightweight coxless four", "5:45.60", "Thomas Poulsen\\nThomas Ebert\\nEskild Ebbesen\\nVictor Feddersen", "Denmark", "1999", "", "Lucerne, Switzerland"], ["M4x\\nQuad sculls", "5:33.15", "Vladislav Ryabcev\\nAlexey Svirin\\nNikita Morgachev\\nSergei Fedorovtsev", "Russia", "2012", "", "Lucerne,Switzerland"], ["M2-\\nCoxless pairs", "6:08.50", "Hamish Bond\\nEric Murray", "New Zealand", "2012", "Summer Olympics", "Eton Dorney, England"], ["M4-\\nCoxless four", "5:37.86", "Andrew Triggs-Hodge\\nTom James\\nPete Reed\\nAlex Gregory", "Great Britain", "2012", "", "Lucerne, Switzerland"], ["LM8+\\nLightweight eight", "5:30.24", "Klaus Altena\\nChristian Dahlke\\nThomas Melges\\nBernhard Stomporowski\\nMichael Kobor\\nUwe Maerz\\nMichael Buchheit\\nKai von Warburg\\nOlaf Kaska (coxswain)", "Germany", "1992", "", "Montreal, Canada"], ["M2+\\nCoxed pairs", "6:42.16", "Igor Boraska\\nTihomir Franković\\nMilan Razov (coxswain)", "Croatia", "1994", "", "Indianapolis, United States"], ["M4+\\nCoxed four", "5:58.96", "Matthias Ungemach\\nArmin Eichholz\\nArmin Weyrauch\\nBahne Rabe\\nJoerg Dederding (coxswain)", "Germany", "1991", "", "Vienna, Austria"], ["M8+\\nEight", "5:19.35", "Gabriel Bergen\\nDouglas Csima\\nRobert Gibson\\nConlin McCabe\\nMalcolm Howard\\nAndrew Byrnes\\nJeremiah Brown\\nWill Crothers\\nBrian Price (coxswain)", "Canada", "2012", "", "Lucerne, Switzerland"]] | 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 fastest rowing time in the world for a lm2x lightweight double scull boat? | 6:10.02 | 128 | Answer: |
Table InputTable: [["Year", "Competition", "Venue", "Position", "Event", "Notes"], ["1987", "World Championships", "Rome, Italy", "13th", "Marathon", "2:17:45"], ["1990", "European Championships", "Split, FR Yugoslavia", "4th", "Marathon", "2:17:45"], ["1992", "Olympic Games", "Barcelona, Spain", "5th", "Marathon", "2:14:15"], ["1993", "World Championships", "Stuttgart, Germany", "—", "Marathon", "DNF"], ["1986", "Venice Marathon", "Venice, Italy", "1st", "Marathon", "2:18:44"], ["1987", "Venice Marathon", "Venice, Italy", "1st", "Marathon", "2:10:01"], ["1991", "World Championships", "Tokyo, Japan", "6th", "Marathon", "2:15:58"], ["1996", "Olympic Games", "Atlanta, United States", "20th", "Marathon", "2:17:27"]] | 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 salvatore bettiol finish at least in the top five position? | 4 | 128 | Answer: |
Table InputTable: [["County", "Brown", "Votes", "Nixon", "Votes", "Wyckoff", "Votes"], ["Lassen", "62.50%", "3,500", "35.14%", "1,968", "2.36%", "132"], ["Kings", "59.03%", "9,141", "39.48%", "6,113", "1.49%", "231"], ["Sutter", "41.19%", "4,816", "57.59%", "6,734", "1.21%", "142"], ["Placer", "59.98%", "13,592", "38.29%", "8,677", "1.72%", "390"], ["San Francisco", "62.19%", "180,298", "36.96%", "107,165", "0.85%", "2,455"], ["Siskiyou", "59.98%", "7,718", "38.41%", "4,942", "1.62%", "208"], ["Imperial", "44.14%", "8,241", "55.01%", "10,271", "0.85%", "158"], ["Del Norte", "51.97%", "2,741", "45.85%", "2,418", "2.18%", "115"], ["Colusa", "52.06%", "2,320", "46.14%", "2,056", "1.80%", "80"], ["Mendocino", "51.50%", "8,704", "46.96%", "7,936", "1.54%", "261"], ["Solano", "64.31%", "25,987", "34.37%", "13,888", "1.32%", "532"], ["Yolo", "60.67%", "13,334", "37.82%", "8,311", "1.51%", "332"], ["Stanislaus", "53.64%", "30,431", "44.80%", "25,417", "1.57%", "888"], ["Calaveras", "46.37%", "2,379", "51.75%", "2,655", "1.87%", "96"], ["Trinity", "64.58%", "2,201", "33.69%", "1,148", "1.73%", "59"], ["Nevada", "51.02%", "4,818", "47.12%", "4,450", "1.85%", "175"], ["Marin", "45.38%", "27,664", "53.67%", "32,720", "0.95%", "582"], ["Alpine", "34.72%", "67", "63.21%", "122", "2.07%", "4"], ["San Benito", "48.30%", "2,527", "50.46%", "2,640", "1.24%", "65"], ["Amador", "58.16%", "2,811", "40.16%", "1,941", "1.68%", "81"], ["Shasta", "63.97%", "14,753", "34.07%", "7,858", "1.96%", "453"], ["Kern", "52.10%", "48,737", "46.33%", "43,342", "1.57%", "1,471"], ["Yuba", "53.77%", "5,028", "44.74%", "4,184", "1.49%", "139"], ["Tulare", "49.08%", "24,598", "49.71%", "24,914", "1.21%", "608"], ["Santa Barbara", "47.50%", "30,424", "51.24%", "32,821", "1.26%", "807"], ["Modoc", "51.73%", "1,641", "46.44%", "1,473", "1.83%", "58"], ["Tehama", "51.36%", "5,077", "46.44%", "4,591", "2.21%", "218"], ["Glenn", "48.70%", "3,299", "49.50%", "3,353", "1.80%", "122"], ["Inyo", "47.00%", "2,526", "50.99%", "2,740", "2.01%", "108"], ["Lake", "44.42%", "3,315", "54.15%", "4,041", "1.43%", "107"], ["El Dorado", "56.25%", "6,572", "41.44%", "4,842", "2.30%", "269"]] | 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 country did wyckoff receive the least votes? | Alpine | 128 | Answer: |
Table InputTable: [["Date", "Site", "Winning team", "Winning team", "Losing team", "Losing team", "Series"], ["October 31, 2002", "Colorado Springs", "Colorado State", "31", "Air Force", "12", "AFA 23–17–1"], ["September 3, 1994", "Colorado Springs", "Colorado State", "34", "Air Force", "21", "AFA 20–12–1"], ["October 16, 1982", "Colorado Springs", "Colorado State", "21", "Air Force", "11", "AFA 12–8–1"], ["October 17, 1992", "Colorado Springs", "Colorado State", "32", "Air Force", "28", "AFA 20–10–1"], ["September 26, 1987", "Fort Collins", "Air Force", "27", "Colorado State", "19", "AFA 17–8–1"], ["September 30, 1989", "Fort Collins", "Air Force", "46", "Colorado State", "21", "AFA 19–8–1"], ["September 29, 1984", "Colorado Springs", "Air Force", "52", "Colorado State", "10", "AFA 14–8–1"], ["September 27, 1986", "Colorado Springs", "Air Force", "24", "Colorado State", "7", "AFA 16–8–1"], ["September 6, 1980", "Fort Collins", "Colorado State", "21", "Air Force", "9", "AFA 11–7–1"], ["October 31, 2009", "Fort Collins", "Air Force", "34", "Colorado State", "16", "AFA 28–19–1"], ["September 29, 2012", "Colorado Springs", "Air Force", "42", "Colorado State", "21", "AFA 31–19–1"], ["September 20, 1997", "Fort Collins", "Air Force", "24", "Colorado State", "0", "AFA 21–14–1"], ["September 29, 2005", "Fort Collins", "Colorado State", "41", "Air Force", "23", "AFA 24–19–1"], ["September 16, 1995", "Colorado Springs", "Colorado State", "27", "Air Force", "20", "AFA 20–13–1"], ["October 3, 1981", "Colorado Springs", "Air Force", "28", "Colorado State", "14", "AFA 12–7–1"], ["September 3, 1988", "Fort Collins", "Air Force", "29", "Colorado State", "23", "AFA 18–8–1"], ["September 3, 1983", "Fort Collins", "Air Force", "34", "Colorado State", "13", "AFA 13–8–1"], ["November 18, 1999", "Fort Collins", "Colorado State", "41", "Air Force", "21", "AFA 22–15–1"], ["November 2, 1996", "Colorado Springs", "Colorado State", "42", "Air Force", "41", "AFA 20–14–1"], ["November 8, 2008", "Colorado Springs", "Air Force", "38", "Colorado State", "17", "AFA 27–19–1"], ["September 7, 1991", "Fort Collins", "Air Force", "31", "Colorado State", "26", "AFA 20–9–1"], ["September 17, 1998", "Colorado Springs", "Air Force", "30", "Colorado State", "27", "AFA 22–14–1"], ["November 20, 2004", "Colorado Springs", "Air Force", "47", "Colorado State", "17", "AFA 24–18–1"], ["October 12, 2006", "Colorado Springs", "Air Force", "24", "Colorado State", "21", "AFA 25–19–1"], ["November 30, 2013", "Fort Collins", "Colorado State", "58", "Air Force", "13", "AFA 31–20–1"], ["October 9, 2010", "Colorado Springs", "Air Force", "49", "Colorado State", "27", "AFA 29–19–1"], ["November 8, 2001", "Fort Collins", "Colorado State", "28", "Air Force", "21", "AFA 23–16–1"]] | 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 is the largest margin of victory for colorado state in the trophy era? | 58-13 | 128 | Answer: |
Table InputTable: [["Designation", "Keel Laid", "Launched", "Commissioned", "Fate"], ["PE-5", "28 May 1918", "28 September 1918", "19 November 1918", "Sold 11 June 1930"], ["PE-4", "21 May 1918", "15 September 1918", "14 November 1918", "Sold 11 June 1930"], ["PE-3", "16 May 1918", "11 September 1918", "11 November 1918", "Sold 11 June 1930"], ["PE-1", "7 May 1918", "11 July 1918", "27 October 1918", "Sold 11 June 1930"], ["PE-2", "10 May 1918", "19 August 1918", "11 July 1918", "Sold 11 June 1930"], ["PE-8", "10 June 1918", "11 November 1918", "31 October 1919", "Sold 1 April 1931"], ["PE-25", "17 September 1918", "19 February 1919", "30 June 1919", "Capsized in Delaware Bay squall 11 June 1920"], ["PE-9", "17 June 1918", "8 November 1918", "27 October 1919", "Sold 26 May 1930"], ["PE-32", "30 November 1918", "15 March 1919", "4 September 1919", "In service during WWII\\nSold 3 March 1947"], ["PE-27", "22 October 1918", "1 March 1919", "14 July 1919", "In service during WWII\\nSold 4 June 1946"], ["PE-11", "13 July 1918", "14 November 1918", "29 May 1919", "Sold 16 January 1935"], ["PE-19", "6 August 1918", "30 January 1919", "25 June 1919", "In service during WWII\\nDestroyed 6 August 1946"], ["PE-31", "19 November 1918", "8 March 1919", "14 August 1919", "Sold 18 May 1923"], ["PE-7", "8 June 1918", "5 October 1918", "24 November 1918", "Expended as target 30 November 1934"], ["PE-6", "3 June 1918", "16 October 1918", "21 November 1918", "Expended as target 30 November 1934"], ["PE-38", "30 January 1919", "29 March 1919", "30 July 1919", "In service during WWII\\nSold 3 March 1947"], ["PE-24", "13 September 1918", "24 February 1919", "12 July 1919", "Sold 11 June 1930"], ["PE-12", "13 July 1918", "12 November 1918", "6 November 1919", "Sold 30 December 1935"], ["PE-55", "17 March 1919", "22 July 1919", "10 October 1919", "In service during WWII\\nSold 3 March 1947"], ["PE-57", "25 March 1919", "29 July 1919", "15 October 1919", "In service during WWII\\nSold 5 March 1947"], ["PE-18", "5 August 1918", "10 February 1919", "7 August 1919", "Sold 11 June 1930"], ["PE-26", "25 September 1918", "1 March 1919", "1 October 1919", "Sold 29 August 1938"], ["PE-23", "11 September 1918", "20 February 1919", "19 June 1919", "Sold 11 June 1930"], ["PE-10", "6 July 1918", "9 November 1918", "31 October 1919", "Destroyed 19 August 1937"], ["PE-29", "18 November 1918", "8 March 1919", "20 August 1919", "Sold 11 June 1930"], ["PE-28", "23 October 1918", "1 March 1919", "28 July 1919", "Sold 11 June 1930"], ["PE-13", "15 July 1918", "9 January 1919", "2 April 1919", "Sold 26 May 1930"], ["PE-16", "22 July 1918", "11 January 1919", "5 June 1919", "Transferred to USCG late 1919"]] | 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 keels were laid n the month of may 1918? | 5 | 128 | Answer: |
Table InputTable: [["Year", "Award", "Nominated work", "Category", "Result"], ["2008", "Britain's Best", "Leona Lewis", "Music Award", "Won"], ["2007", "Cosmopolitan Ultimate Woman of the Year", "Leona Lewis", "Newcomer of the Year", "Won"], ["2008", "Glamour Woman Of The Year Awards", "Leona Lewis", "UK Solo Artist", "Won"], ["2008", "Capital Awards", "Leona Lewis", "Favourite UK Female Artist", "Won"], ["2009", "BEFFTA Awards", "Leona Lewis", "Best Female Act", "Won"], ["2008", "NewNowNext Awards", "Leona Lewis", "The Kylie Award: Next International Crossover", "Won"], ["2009", "Cosmopolitan Awards", "Leona Lewis", "Ultimate Music Star", "Won"], ["2008", "Billboard 2008 Year End Award", "Leona Lewis", "Best New Artist", "Won"], ["2008", "PETA", "Leona Lewis", "Person Of The Year", "Won"], ["2009", "NAACP Image Awards", "Leona Lewis", "Outstanding New Artist", "Nominated"], ["2008", "Bambi Award", "Leona Lewis", "Shooting Star", "Won"], ["2009", "Swiss Music Awards", "Leona Lewis", "Best International Newcomer", "Won"], ["2008", "New Music Weekly Awards", "Leona Lewis", "Top 40 New Artist of the Year", "Won"], ["2009", "Japan Gold Disc Awards", "Leona Lewis", "New Artist Of The Year", "Won"], ["2009", "PETA - Sexiest Vegetarian Alive Awards", "Leona Lewis", "Sexiest Vegetarian Celebrity 2009", "Won"], ["2009", "APRA Awards", "\"Bleeding Love\"", "Most Played Foreign Work", "Won"], ["2008", "UK Music Video Awards", "\"Bleeding Love\"", "People's Choice Award", "Won"], ["2007", "The Record of the Year", "\"Bleeding Love\"", "The Record of the Year", "Won"], ["2009", "HITO Pop Music Awards", "\"Bleeding Love\"", "Best Western Song", "Won"], ["2008", "Vh1 Video of the Year", "\"Bleeding Love\"", "Best Video", "Won"], ["2008", "NME Best Album", "\"Spirit\"", "Best Album", "Nominated"], ["2008", "Nickelodeon UK Kids Choice Awards", "\"Bleeding Love\"", "Favourite Song", "Won"]] | 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 award leona lewis won? | Cosmopolitan Ultimate Woman of the Year | 128 | Answer: |
Table InputTable: [["Week", "Date", "Opponent", "Results\\nFinal score", "Results\\nTeam record", "Venue", "Attendance"], ["6", "October 23", "at Chicago Bears", "L 13–10", "2–4", "Soldier Field", "55,701"], ["1", "September 18", "Washington Redskins", "L 24–21", "0–1", "Metropolitan Stadium", "47,900"], ["12", "December 3", "Chicago Bears", "W 23–10", "7–5", "Metropolitan Stadium", "49,784"], ["11", "November 26", "at Pittsburgh Steelers", "L 23–10", "6–5", "Three Rivers Stadium", "50,348"], ["9", "November 12", "Detroit Lions", "W 16–14", "5–4", "Metropolitan Stadium", "49,784"], ["7", "October 29", "at Green Bay Packers", "W 27–13", "3–4", "Lambeau Field", "56,263"], ["4", "October 8", "St. Louis Cardinals", "L 19–17", "1–3", "Metropolitan Stadium", "49,687"], ["13", "December 10", "Green Bay Packers", "L 23–7", "7–6", "Metropolitan Stadium", "49,784"], ["8", "November 5", "New Orleans Saints", "W 37–6", "4–4", "Metropolitan Stadium", "49,784"], ["2", "September 24", "at Detroit Lions", "W 34–10", "1–1", "Tiger Stadium", "54,418"], ["3", "October 1", "Miami Dolphins", "L 16–14", "1–2", "Metropolitan Stadium", "47,900"], ["14", "December 16", "at San Francisco 49ers", "L 20–17", "7–7", "Candlestick Park", "61,214"], ["10", "November 19", "at Los Angeles Rams", "W 45–41", "6–4", "Los Angeles Memorial Coliseum", "77,982"], ["5", "October 15", "at Denver Broncos", "W 23–20", "2–3", "Mile High Stadium", "51,656"]] | 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 date was the only game played on soldier field? | October 23 | 128 | Answer: |
Table InputTable: [["Hand", "Auto", "Wind", "Athlete", "Nationality", "Birthdate", "Location", "Date"], ["", "10.90", "", "Thaddeus Bell", "United States", "28.11.1942", "Raleigh", "01.05.1988"], ["", "10.26", "2.3", "Troy Douglas", "Netherlands", "30.11.1962", "Utrecht", "10.07.2004"], ["", "10.84", "1.8", "Erik Oostweegel", "Netherlands", "29.04.1960", "Tilburg", "10.06.2000"], ["", "10.93", "0.6", "Gilles Echevin", "France", "01.09.1948", "Grenoble", "07.05.1989"], ["", "10.95", "", "George McNeill", "United Kingdom", "19.02.1947", "Melbourne", "31.11.1987"], ["", "10.29", "1.9", "Troy Douglas", "Netherlands", "30.11.1962", "Leiden", "07.06.2003"], ["", "10.87", "", "Eddie Hart", "United States", "24.04.1949", "Eugene", "03.08.1989"], ["", "10.95", "", "Karl Heinz Schröder", "Germany", "17.06.1939", "Hannover", "28.07.1979"], ["", "10.60", "", "Bill Collins", "United States", "20.11.1950", "", "06.06.1992"], ["10.7", "", "", "Thane Baker", "United States", "04.10.1931", "Elkhart", "13.09.1972"], ["10.7", "", "", "Klaus Jürgen Schneider", "Germany", "02.03.1942", "Stuttgart", "07.07.1982"], ["10.7", "", "", "Walt Butler", "United States", "21.03.1941", "Northridge", "16.05.1981"]] | 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:according to the chart, who was the last one born? | Troy Douglas | 128 | Answer: |
Table InputTable: [["Rank", "Country", "Box Office", "Year", "Box office\\nfrom national films"], ["1", "Canada/United States", "$10.8 billion", "2012", "–"], ["10", "Australia", "$1.2 billion", "2012", "4.1% (2011)"], ["4", "United Kingdom", "$1.7 billion", "2012", "36.1% (2011)"], ["-", "World", "$34.7 billion", "2012", "–"], ["7", "India", "$1.4 billion", "2012", "–"], ["8", "Germany", "$1.3 billion", "2012", "–"], ["12", "Brazil", "$0.72 billion", "2013", "17% (2013)"], ["2", "China", "$3.6 billion", "2013", "59% (2013)"], ["9", "Russia", "$1.2 billion", "2012", "–"], ["5", "France", "$1.7 billion", "2012", "33.3% (2013)"], ["3", "Japan", "$1.88 billion", "2013", "61% (2013)"], ["11", "Italy", "$0.84 billion", "2013", "30% (2013)"], ["6", "South Korea", "$1.47 billion", "2013", "59.7% (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:canada, the united states, and australia accounted for how much box office revenue in 2012? | $12 billion | 128 | Answer: |
Table InputTable: [["Year", "Division", "League", "Reg. Season", "Playoffs", "National Cup"], ["1945/46", "N/A", "ASL", "1st", "Champion (no playoff)", "?"], ["1939/40", "N/A", "ASL", "2nd(t)", "No playoff", "Co-champion"], ["1935/36", "N/A", "ASL", "2nd", "No playoff", "?"], ["1947/48", "N/A", "ASL", "4th", "No playoff", "?"], ["1946/47", "N/A", "ASL", "4th", "No playoff", "?"], ["1941/42", "N/A", "ASL", "5th", "No playoff", "?"], ["1944/45", "N/A", "ASL", "4th", "No playoff", "?"], ["1942/43", "N/A", "ASL", "5th", "No playoff", "?"], ["1934/35", "N/A", "ASL", "6th", "No playoff", "?"], ["1943/44", "N/A", "ASL", "3rd", "No playoff", "?"], ["1940/41", "N/A", "ASL", "3rd", "No playoff", "?"], ["1948/49", "N/A", "ASL", "Withdrew after 3 games", "N/A", "N/A"], ["1936/37", "N/A", "ASL", "2nd, American", "1st Round", "?"], ["1937/38", "N/A", "ASL", "4th, American", "Did not qualify", "?"], ["1938/39", "N/A", "ASL", "5th, American", "Did not qualify", "?"]] | 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 teams top finish in the regular season? | 1st | 128 | Answer: |
Table InputTable: [["Year", "Car", "Start", "Qual", "Rank", "Finish", "Laps", "Led", "Retired"], ["1928", "8", "4", "117.031", "4", "10", "200", "33", "Running"], ["1939", "62", "27", "121.749", "24", "11", "200", "0", "Running"], ["1927", "27", "27", "107.765", "22", "3", "200", "0", "Running"], ["1929", "23", "11", "112.146", "15", "17", "91", "0", "Supercharger"], ["1933", "34", "12", "113.578", "15", "7", "200", "0", "Running"], ["1937", "38", "7", "118.788", "16", "8", "200", "0", "Running"], ["1934", "8", "7", "113.733", "13", "17", "94", "0", "Rod"], ["1931", "37", "19", "111.725", "6", "18", "167", "0", "Crash T4"], ["1938", "17", "4", "122.499", "6", "17", "130", "0", "Rod"], ["1930", "9", "20", "100.033", "18", "20", "79", "0", "Valve"], ["1926", "31", "12", "102.789", "13", "11", "142", "0", "Flagged"], ["1935", "44", "6", "115.459", "11", "21", "102", "0", "Magneto"], ["1932", "25", "20", "108.896", "34", "13", "184", "0", "Flagged"], ["Totals", "Totals", "Totals", "Totals", "Totals", "Totals", "1989", "33", ""]] | 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 tony gulotta's highest finishing rank in his indy 500 career? | 3 | 128 | Answer: |
Table InputTable: [["Heat", "Rank", "Name", "Result", "Notes"], ["1", "15", "Kate Anderson (AUS)", "15:36.16 q", ""], ["1", "16", "Yelena Kopytova (RUS)", "15:37.19", "PB"], ["1", "2", "Paula Radcliffe (GBR)", "15:27.25 Q", ""], ["2", "10", "Lydia Cheromei (KEN)", "15:32.00 Q", ""], ["2", "3", "Fernanda Ribeiro (POR)", "15:27.30 Q", ""], ["2", "18", "Stela Olteanu (ROU)", "15:40.86", ""], ["1", "9", "Libbie Hickman (USA)", "15:30.56 q", "SB"], ["1", "19", "Adriana Fernandez (MEX)", "15:41.55", ""], ["1", "26", "Zohra Ouaziz (MAR)", "15:58.84", ""], ["1", "1", "Gabriela Szabo (ROU)", "15:26.62 Q", ""], ["1", "21", "Melody Fairchild (USA)", "15:47.66", ""], ["1", "23", "Marina Bastos (POR)", "15:54.01", ""], ["2", "14", "Yuko Kawakami (JPN)", "15:32.71 Q", ""], ["2", "20", "Olivera Jevtić (YUG)", "15:43.76", ""], ["1", "4", "Harumi Hiroyama (JPN)", "15:27.75 Q", ""], ["2", "27", "Amy Rudolph (USA)", "16:00.87", ""], ["1", "33", "Justine Nahimana (BUR)", "17:21.77", ""], ["2", "6", "Liu Jianying (CHN)", "15:29.28 Q", "PB"], ["1", "29", "Laurence Duquenoy (FRA)", "16:06.02", ""], ["2", "13", "Naoko Takahashi (JPN)", "15:32.25 Q", ""], ["1", "5", "Roberta Brunet (ITA)", "15:29.03 Q", ""], ["2", "32", "Helena Javornik (SLO)", "16:28.38", ""], ["2", "17", "Sonia O'Sullivan (IRL)", "15:40.82", ""], ["1", "35", "Zalia Aliou (TOG)", "18:34.45", "NR"], ["2", "24", "Restituta Joseph (TAN)", "15:55.22", "NR"], ["1", "25", "Valerie Vaughan (IRL)", "15:57.58", ""], ["2", "31", "Jelena Chelnova (LAT)", "16:27.63", ""], ["1", "12", "Gunhild Hall (NOR)", "15:32.13 q", ""], ["1", "28", "Genet Gebregiorgis (ETH)", "16:04.40", "SB"], ["2", "—", "Annemari Sandell (FIN)", "DNS", ""], ["2", "—", "Anne Hare (NZL)", "DNF", ""], ["1", "8", "Li Wei (CHN)", "15:29.62 Q", ""], ["2", "11", "Merima Denboba (ETH)", "15:32.01 Q", ""], ["1", "7", "Ayelech Worku (ETH)", "15:29.37 Q", ""], ["2", "22", "Chrystosomia Iakovou (GRE)", "15:51.14", ""], ["2", "—", "Kristina da Fonseca-Wollheim (GER)", "DNF", ""], ["1", "34", "Nebiat Habtemariam (ERI)", "18:26.50", ""], ["1", "36", "Martha Portobanco (NCA)", "19:08.44", ""], ["1", "—", "Maysa Matrood (IRQ)", "DNS", ""], ["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 got 15th place? | Kate Anderson | 128 | Answer: |
Table InputTable: [["Contestant", "Original\\nTribe", "First\\nSwitch", "Second\\nSwitch", "Merged\\nTribe", "Finish", "Ghost\\nIsland", "Total\\nVotes"], ["Milena Vitanović\\n21, Paraćin", "Ga 'dang", "", "", "", "4th Voted Out\\nDay 13", "4th Eliminated\\nDay 18", "8"], ["Branka Čudanov\\n28, Kikinda", "Ga 'dang", "", "", "", "2nd Voted Out\\nDay 7", "1st Eliminated\\nDay 9", "10"], ["Njegoš Arnautović\\n21, Bijeljina, Republika Srpska", "Manobo", "Ga 'dang", "Ga 'dang", "Diwata", "Eliminated in Challenge\\n7th Jury Member\\nDay 53", "Locator of\\nHidden Immunity Idol\\n(Successful)\\nDay 40", "1"], ["Luka Rajačić\\n21, Belgrade", "Ga 'dang", "Manobo", "Manobo", "", "9th Voted Out\\nDay 31", "10th Eliminated\\nDay 32", "6"], ["Ana Mitrić\\n23, Belgrade", "Ga 'dang", "", "", "", "3rd Voted Out\\nDay 10", "3rd Eliminated\\nDay 15", "7"], ["Dušan Milisavljević\\n25, Zvečan", "Manobo", "Manobo", "Manobo", "Diwata", "Eliminated in Challenge\\n8th Jury Member\\nDay 53", "Locator of\\nHidden Immunity Idol\\n(Failed)\\nDay 37", "2"], ["Gordana Berger\\n38, Belgrade", "Manobo", "", "", "", "1st Voted Out\\nDay 4", "2nd Eliminated\\nDay 12", "9"], ["Ana Stojanovska\\n21, Skopje, Macedonia", "Manobo", "Manobo", "Manobo", "", "8th Voted Out\\nDay 28", "9th Eliminated\\nDay 30", "3"], ["Klemen Rutar\\n21, Ljubljana, Slovenija", "Manobo", "Ga 'dang", "Ga 'dang", "Diwata", "14th Voted Out\\n5th Jury Member\\nDay 47", "Locator of\\nHidden Immunity Idol\\n(Failed)\\nDay 34", "6"], ["Pece Kotevski\\n42, Bitola, Macedonia", "Ga 'dang", "Manobo", "", "", "6th Voted Out\\nDay 19", "6th Eliminated\\nDay 21", "7"], ["Nikola Kovačević\\n24, Kragujevac", "Ga 'dang", "", "", "Diwata", "11th Voted Out\\n2nd Jury Member\\nDay 38", "Ghost Island Winner\\nDay 32", "12"], ["Vesna Đolović\\n38, Beograd", "Manobo", "Manobo", "Manobo", "Diwata", "2nd Runner-Up", "Locator of\\nHidden Immunity Idol\\n(Failed)\\nDay 46", "8"], ["Aleksandar Bošković\\n28, Belgrade", "Manobo", "Manobo", "", "", "7th Voted Out\\nDay 25", "8th Eliminated\\nDay 27", "4"], ["Dina Berić\\n23, Ledinci, near Novi Sad", "Manobo", "Ga 'dang", "Manobo", "Diwata", "12th Voted Out\\n3rd Jury Member\\nDay 41", "", "6"], ["Višnja Banković\\n24, Aranđelovac", "Ga 'dang", "Ga 'dang", "Ga 'dang", "Diwata", "13th Voted Out\\n4th Jury Member\\nDay 44", "", "14"], ["Aleksandar Krajišnik\\n19, Majur, near Šabac", "Ga 'dang", "Ga 'dang", "Ga 'dang", "Diwata", "Sole Survivor", "Locator of\\nHidden Immunity Idol\\n(Failed)\\nDay 43", "0"], ["Teja Lapanja\\n30, Škofja Loka, Slovenija", "Ga 'dang", "Ga 'dang", "Ga 'dang", "Diwata", "Runner-Up", "Locator of\\nHidden Immunity Idol\\n(Failed)\\nDay 49", "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:who was the last contestant voted out? | Srđan Dinčić | 128 | Answer: |
Table InputTable: [["Rank", "Player", "Nation", "Club", "Goals"], ["2", "Matthew Delicâte", "ENG", "Richmond Kickers", "10"], ["1", "Jhonny Arteaga", "COL", "FC New York", "13"], ["3", "José Angulo", "USA", "Harrisburg City Islanders", "9"], ["9", "Chris Banks", "USA", "Wilmington Hammerheads", "7"], ["9", "George Davis IV", "USA", "Dayton Dutch Lions", "7"], ["3", "Luke Mulholland", "ENG", "Wilmington Hammerheads", "9"], ["6", "Andriy Budnyy", "UKR", "Wilmington Hammerheads", "8"], ["6", "Andrew Welker", "USA", "Harrisburg City Islanders", "8"], ["9", "Sainey Touray", "GAM", "Harrisburg City Islanders", "7"], ["9", "Sallieu Bundu", "SLE", "Charlotte Eagles", "7"], ["3", "Maxwell Griffin", "USA", "Orlando City", "9"], ["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:which players came in last? | Chris Banks, Sallieu Bundu, George Davis IV, Sainey Touray | 128 | Answer: |
Table InputTable: [["District", "Representative", "Party", "Home Town, County", "Term of Service"], ["1st District", "Charles R. Blasdel", "Republican", "East Liverpool, Columbiana", ""], ["76th District", "Michael E. Gilb", "Republican", "Findlay, Hancock", ""], ["4th District", "John R. Willamowski", "Republican", "Lima, Allen", ""], ["25th District", "Daniel Stewart", "Democratic", "Columbus, Franklin", ""], ["27th District", "Joyce Beatty", "Democratic", "Columbus, Franklin", ""], ["23rd District", "Larry Wolpert", "Republican", "Hilliard, Franklin", ""], ["54th District", "Courtney E. Combs", "Republican", "Hamilton, Butler", ""], ["24th District", "Geoffrey C. Smith", "Republican", "Columbus, Franklin", ""], ["45th District", "Robert J. Otterman", "Democratic", "Akron, Summit", ""], ["21st District", "Linda Reidelbach", "Republican", "Columbus, Franklin", ""], ["57th District", "Earl J. Martin", "Republican", "Avon Lake, Lorain", ""], ["99th District", "L. George Distel", "Democratic", "Conneaut, Ashtabula", ""], ["51st District", "W. Scott Oelslager", "Republican", "Canton, Stark", ""], ["26th District", "Larry Price", "Democratic", "Columbus, Franklin", "-2005"], ["86th District", "David T. Daniels", "Republican", "Greenfield, Highland", ""], ["74th District", "Stephen P. Buehrer", "Republican", "Delta, Fulton", ""], ["18th District", "Thomas F. Patton", "Republican", "Strongsville, Cuyahoga", ""], ["38th District", "John J. White", "Republican", "Kettering, Montgomery", ""], ["37th District", "Jon A. Husted", "Republican", "Kettering, Montgomery", ""], ["70th District", "Kevin DeWine", "Republican", "Fairborn, Greene", ""], ["64th District", "Daniel J. Sferra", "Democratic", "Warren, Trumbull", "2005-"], ["33rd District", "Tyrone K. Yates", "Democratic", "Cincinnati, Hamilton", ""], ["2nd District", "Jon M. Peterson", "Republican", "Delaware, Delaware", ""], ["82nd District", "Stephen Reinhard", "Republican", "Bucyrus, Crawford", ""], ["5th District", "Tim Schaffer", "Republican", "Lancaster, Fairfield", ""], ["36th District", "Arlene J. Setzer", "Republican", "Vandalia, Montgomery", ""], ["97th District", "Bob Gibbs", "Republican", "Lakeville, Holmes\\n<", ""], ["39th District", "Dixie J. Allen", "Republican", "Dayton, Montgomery", ""], ["61st District", "John A. Boccieri", "Democratic", "New Middletown, Mahoning", ""], ["47th District", "Peter Ujvagi", "Democratic", "Toledo, Lucas", ""], ["46th District", "Lynn E. Olman", "Republican", "Maumee, Lucas", "-2005"], ["53rd District", "Shawn N. Webster", "Republican", "Millville, Butler", ""], ["6th District", "Robert E. Latta", "Republican", "Bowling Green, Wood", ""], ["52nd District", "Mary M. Cirelli", "Democratic", "Canton, Stark", "-2005"], ["22nd District", "Jim Hughes", "Republican", "Columbus, Franklin", ""], ["84th District", "Chris Widener", "Republican", "Springfield, Clark", ""], ["44th District", "Barbara A. Sykes", "Democratic", "Akron, Summit", ""], ["73rd District", "William J. Hartnett", "Democratic", "Mansfield, Richland", ""], ["40th District", "Fred Strahorn", "Democratic", "Dayton, Montgomery", ""], ["3rd District", "Jim Carmichael", "Republican", "Wooster, Wayne", ""], ["71st District", "David R. Evans", "Republican", "Newark, Licking", ""], ["91st District", "Larry Householder", "Republican", "Glenford, Perry", "-2005"]] | 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 representative from the first district? | Charles R. Blasdel | 128 | Answer: |
Table InputTable: [["Year", "Network", "Race caller", "Color commentator", "Reporters", "Trophy Presentation"], ["1959", "CBS", "Fred Capossela", "Bryan Field and Chris Schenkel", "", "Chris Schenkel"], ["1958", "CBS", "Fred Capossela", "Bryan Field", "", ""], ["1957", "CBS", "Fred Capossela", "Bryan Field", "", ""], ["1956", "CBS", "Fred Capossela", "Bryan Field", "", ""], ["1955", "CBS", "Fred Capossela", "Phil Sutterfield and Win Elliot", "", ""], ["1954", "CBS", "Bryan Field", "Mel Allen", "", "Bill Corum"], ["1953", "CBS", "Bryan Field", "Mel Allen", "Phil Sutterfield", "Phil Sutterfield"], ["1952", "CBS", "Bryan Field", "Sam Renick", "", ""]] | 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 consecutive years was fred capossela the race caller? | 5 | 128 | Answer: |
Table InputTable: [["Season", "Tier", "Division", "Place"], ["1991/92", "3", "2ªB", "12th"], ["1996/97", "4", "3ª", "2nd"], ["1995/96", "3", "2ªB", "19th"], ["1992/93", "3", "2ªB", "4th"], ["1990/91", "3", "2ªB", "6th"], ["1993/94", "3", "2ªB", "15th"], ["1988/89", "4", "3ª", "3rd"], ["1994/95", "3", "2ªB", "9th"], ["1998/99", "4", "3ª", "6th"], ["1989/90", "4", "3ª", "1st"], ["1997/98", "4", "3ª", "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:what is the number of divisions for 2ab? | 6 | 128 | Answer: |
Table InputTable: [["Year", "Total\\npassengers", "Passenger\\nChange", "Domestic", "International\\n(total)", "International\\n(non-CIS)", "CIS", "Aircraft\\nLandings", "Cargo\\n(tonnes)"], ["2007", "2 345 097", "+32,9%", "1 486 888", "858 209", "683 092", "175 117", "16 767", "16 965"], ["2004", "1 553 628", "+16,3%", "972 287", "581 341", "429 049", "152 292", "11 816", "20 457"], ["2001", "1 028 295", "+10,5%", "733 022", "295 273", "186 861", "108 412", "9 062", "22 178"], ["2003", "1 335 757", "+12,9%", "879 665", "456 092", "297 421", "158 671", "10 092", "18 054"], ["2008", "2 529 395", "+7,8%", "1 523 102", "1 006 293", "815 124", "191 169", "16 407", "17 142"], ["2006", "1 764 948", "+12,7%", "1 128 489", "636 459", "488 954", "147 505", "13 289", "15 519"], ["2002", "1 182 815", "+15,0%", "793 295", "389 520", "239 461", "150 059", "10 162", "20 153"], ["2005", "1 566 792", "+0,8%", "1 006 422", "560 370", "429 790", "130 580", "11 877", "11 545"], ["2011", "3 355 883", "+22,1%", "1 856 948", "1 498 935", "1 184 771", "314 164", "20 142", "24 890"], ["2012", "3 783 069", "+12.7%", "1 934 016", "1 849 053", "1 448 765", "439 668", "21 728", "25 866"], ["2009", "2 169 136", "−14,2%", "1 290 639", "878 497", "727 718", "150 779", "13 798", "13 585"], ["2000", "930 251", "+2%", "698 957", "231 294", "155 898", "75 396", "8 619", "18 344"], ["2013", "4 293 002", "+13.5%", "2 180 227", "2 112 775", "", "", "25 728", "27 800"], ["2010", "2 748 919", "+26,7%", "1 529 245", "1 219 674", "1 017 509", "202 165", "15 989", "22 946"]] | 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 year with the most passengers before 2007 | 2006 | 128 | Answer: |
Table InputTable: [["Year", "Title", "Role", "Notes"], ["2006–2007", "Family Guy", "Esther", "Voice\\n3 episodes"], ["2004–2006", "Strong Medicine", "Dr. Kayla Thorton", "37 episodes"], ["1994–1999", "Sister, Sister", "Tamera Campbell", "119 episodes"], ["1999", "Detention", "Orangejella LaBelle", "13 episodes"], ["1991", "Flesh'n Blood", "Penelope", "1 episode"], ["1992", "True Colors", "Lorae", "1 episode"], ["1995–1996", "The Adventures of Hyperman", "Emma C. Squared", "8 episodes"], ["1997", "Smart Guy", "Roxanne", "1 episode"], ["2000", "How I Loved a Macho Boy", "Jamal Santos", "3 episodes"], ["2009", "Roommates", "Hope", "13 episodes"], ["1998", "Blues Clues", "Herself", "1 episode"], ["2011", "Things We Do for Love", "Lourdes", "5 episodes"], ["2014", "Melissa and Joey", "Gillian", "Season 3 Episode 24 'To Tell the Truth'"], ["1995", "Are You Afraid of the Dark?", "Evil Chameleon", "1 episode"], ["2009", "The Super Hero Squad Show", "Misty Knight", "1 episode"], ["2011", "CHRISJayify", "Herself", "Episode: \"Drugs Are Bad\""], ["1994", "The All-New Mickey Mouse (MMC)", "Herself", "1 episode"], ["2012", "Christmas Angel", "Daphney", ""], ["2013", "The Real", "Herself", "Host"], ["2011–2013", "Tia & Tamera", "Herself", "Executive producer"], ["1996", "All That", "Herself", ""], ["2011", "Access Hollywood Live", "Herself", "Co-host"]] | 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 title held the most episodes? | Sister, Sister | 128 | Answer: |
Table InputTable: [["Year", "Film", "Role", "Language", "Notes"], ["2013", "Bahaddoor", "Anjali", "Kannada", "Filming"], ["2013", "Kaddipudi", "Uma", "Kannada", ""], ["2013", "Dilwala", "Preethi", "Kannada", ""], ["2014", "Endendigu", "", "", "Filming"], ["2009", "Olave Jeevana Lekkachaara", "Rukmini", "Kannada", "Innovative Film Award for Best Actress"], ["2012", "Alemari", "Neeli", "Kannada", ""], ["2012", "Drama", "Nandini", "Kannada", ""], ["2012", "18th Cross", "Punya", "Kannada", ""], ["2012", "Sagar", "Kajal", "Kannada", ""], ["2009", "Love Guru", "Kushi", "Kannada", "Filmfare Award for Best Actress - Kannada"], ["2008", "Moggina Manasu", "Chanchala", "Kannada", "Filmfare Award for Best Actress - Kannada\\nKarnataka State Film Award for Best Actress"], ["2012", "Breaking News", "Shraddha", "Kannada", ""], ["2010", "Gaana Bajaana", "Radhey", "Kannada", ""], ["2012", "Addhuri", "Poorna", "Kannada", "Udaya Award for Best Actress\\nNominated — SIIMA Award for Best Actress\\nNominated — Filmfare Award for Best Actress – Kannada"], ["2010", "Krishnan Love Story", "Geetha", "Kannada", "Filmfare Award for Best Actress - Kannada\\nUdaya Award for Best Actress"], ["2014", "Mr. & Mrs. Ramachari", "", "", "Announced"], ["2011", "Hudugaru", "Gayithri", "Kannada", "Nominated, Filmfare Award for Best Actress – Kannada"]] | 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 movies were made between 2009 and 2013? | 14 | 128 | Answer: |
Table InputTable: [["Eps #", "Prod #", "Title", "Summary", "Air Date"], ["13", "19", "Rama-Tut", "After coming back from vacation Reed tells Ben an interesting theory on attempting to restore him. They head to Dr. Doom’s deserted castle to use the time machine the doctor left behind. In 2000 B.C the four weaken during a fight and are taken by Pharaoh Rama-Tut, who is a lot more than he would seem at first sight. Susan is to be Rama-Tut’s queen while the other three are put to work with some mind control. Ben turns back to his former self. As he rescues Susan, he is once again the Thing. The four battle Rama-Tut to his sphinx. Finally, they destroy his sphinx and return to their own time.", "12/9/1967"], ["6", "9", "Prisoners Of Planet X", "A UFO has been sighted. The pilot abducts the Fantastic Four from the Science Center and is setting course for Planet X. There, their dictator Kurrgo requests the Fantastic Four save their planet from another planet knocked off its orbit. Reed manages to formulate a working plan to save the population. While the plan is in process, Kurrgo has other ideas. However, Reed tricks Kurrgo and leaves him on the exploding planet while the micro-sized population and the Fantastic Four get away to safety.", "10/14/1967"], ["12", "13", "Return Of the Mole Man", "The Mole Man is creating earthquakes and causing buildings to sink deep into the Earth. In addition, he and his Moloids kidnap Susan. The Mole Man as usual has been expecting the other three and sends them back to the surface to tell the Army not to get involved. They manage to halt them and seek an alternate entrance in the underworld. Johnny rescues Susan, then they penetrate the laboratory. They all return the buildings to the surface and escape the exploding caves.", "11/25/1967"], ["5a", "1", "Klaws", "Klaw is here to vanquish the Fantastic 4 with his solidifying sonic waves. Johnny is on vacation or so it would seem and arrives in the nick of time to assist Mr. Fantastic in catching The Klaw.", "10/7/1967"], ["3", "7", "The Way It All Began", "While on a television show, Reed recalls the time he first met Victor von Doom before he became Dr. Doom. He had Ben as his roommate at university. Victor was working on dangerous experiments, especially a test that brought him to the hospital and got him expelled from university. Worse than that, the test altered his face and he swore revenge on Reed having to hide his work from him. Ben and Reed became soldiers in World War II. Ben, Susan, Johnny and Reed all went aboard a space rocket for space exploration. And so the origin of the Fantastic Four began. Dr. Doom confronts the Fantastic Four on the television show and briefs them on his origin. After that Dr. Doom attempts to get his revenge, but fails and escapes only to crash.", "9/23/1967"], ["7", "14", "It Started On Yancy Street", "The Fantastic Four face a bunch of old rivals in Yancy Street, but their old enemy Red Ghost and his primates show up and capture them. During their voyage to the moon, the four turn the tables, but Red Ghost gets away and the four are dumped on the moon. They barely manage to get to a source of oxygen which is the Watcher’s laboratory. Using one of the Watcher’s machines, Reed brings down Red Ghost’s ship. Susan gets Dr. Kragoff banished into a trans-nitron machine. Reed uses that machine to get back to Earth.", "10/21/1967"], ["15", "16", "The Micro World Of Dr. Doom", "The Fantastic Four have been shrunken to small size. Dr. Doom is after them and takes them to the Micro World. Dr. Doom briefs them on his micro genius experiments involving a king and a princess from the micro world. The four battle the giant guards but Dr. Doom catches them and imprisons them with the King and Princess. They all escape and enlarge themselves. Ben puts a stop to the Lizard Men, then the four return to their own world.", "12/30/1967"], ["10", "12", "Demon in the Deep", "The Fantastic Four beat the criminal forces working for Dr. Gamma, and blow up the island with its secret weapons. While escaping, Dr. Gamma is infected by the radiation levels in the seabed and morphs into some creature. Johnny is flustered with being moved around and quits from the Fantastic Four. In the town Johnny goes to, there have been sightings of the Gamma Ray. Johnny defeats the Gamma Ray by himself, but he comes back with the hideous giant sea monster Giganto. Johnny rejoins the Fantastic Four. Ben succeeds in eliminating the sea monster. The Gamma Ray is defeated but not finished.", "11/11/1967"]] | 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 episodes aired in 1967? | 16 | 128 | Answer: |
Table InputTable: [["Month", "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec", "Year"], ["Average low °C (°F)", "−17\\n(1)", "−18\\n(0)", "−19\\n(−2)", "−12\\n(10)", "−3\\n(27)", "2\\n(36)", "5\\n(41)", "4\\n(39)", "−1\\n(30)", "−7\\n(19)", "−11\\n(12)", "−14\\n(7)", "−7.6\\n(18.3)"], ["Average high °C (°F)", "−10\\n(14)", "−11\\n(12)", "−12\\n(10)", "−5\\n(23)", "3\\n(37)", "8\\n(46)", "11\\n(52)", "10\\n(50)", "5\\n(41)", "−1\\n(30)", "−5\\n(23)", "−8\\n(18)", "−1.3\\n(29.7)"], ["Mean monthly sunshine hours", "0", "28", "93", "180", "279", "300", "279", "217", "120", "62", "30", "0", "1,588"], ["Daily mean °C (°F)", "−13.5\\n(7.7)", "−14.5\\n(5.9)", "−15.5\\n(4.1)", "−8.5\\n(16.7)", "0\\n(32)", "5\\n(41)", "8\\n(46)", "7\\n(45)", "2\\n(36)", "−4\\n(25)", "−8\\n(18)", "−11\\n(12)", "−4.5\\n(23.9)"], ["Precipitation mm (inches)", "12\\n(0.47)", "13\\n(0.51)", "12\\n(0.47)", "13\\n(0.51)", "16\\n(0.63)", "22\\n(0.87)", "29\\n(1.14)", "26\\n(1.02)", "35\\n(1.38)", "26\\n(1.02)", "23\\n(0.91)", "17\\n(0.67)", "244\\n(9.6)"], ["Avg. precipitation days (≥ 1.0 mm)", "9", "9", "10", "9", "8", "7", "8", "9", "10", "10", "11", "10", "110"]] | 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 months have an average low below 1 degree celsius? | 9 | 128 | Answer: |
Table InputTable: [["Date", "Opponent", "Venue", "Result", "Attendance", "Scorers"], ["19 November 2005", "Aston Villa", "Stadium of Light", "1–3", "39,707", "Whitehead (pen)"], ["29 October 2005", "Portsmouth", "Stadium of Light", "1–4", "34,926", "Whitehead (pen)"], ["7 May 2006", "Aston Villa", "Villa Park", "1–2", "33,820", "D. Collins"], ["3 December 2005", "Tottenham Hotspur", "White Hart Lane", "2–3", "36,244", "Whitehead, Le Tallec"], ["1 October 2005", "West Ham United", "Stadium of Light", "1–1", "31,212", "Miller"], ["22 April 2006", "Portsmouth", "Fratton Park", "1–2", "20,078", "Miller"], ["5 November 2005", "Arsenal", "Highbury", "1–3", "38,210", "Stubbs"], ["1 April 2006", "Everton", "Goodison Park", "2–2", "38,093", "Stead, Delap"], ["31 December 2005", "Everton", "Stadium of Light", "0–1", "30,567", ""], ["25 September 2005", "Middlesbrough", "Riverside Stadium", "2–0", "29,583", "Miller, Arca"], ["12 February 2006", "Tottenham Hotspur", "Stadium of Light", "1–1", "34,700", "Murphy"], ["1 May 2006", "Arsenal", "Stadium of Light", "0–3", "44,003", ""], ["15 October 2005", "Manchester United", "Stadium of Light", "1–3", "39,085", "Elliott"], ["31 January 2006", "Middlesbrough", "Stadium of Light", "0–3", "31,675", ""], ["25 February 2006", "Birmingham City", "St. Andrew's", "0–1", "29,257", ""], ["15 January 2006", "Chelsea", "Stadium of Light", "1–2", "32,420", "Lawrence"], ["26 November 2005", "Birmingham City", "Stadium of Light", "0–1", "32,442", ""], ["20 August 2005", "Liverpool", "Anfield", "0–1", "44,913", ""], ["14 April 2006", "Manchester United", "Old Trafford", "0–0", "72,519", ""], ["30 November 2005", "Liverpool", "Stadium of Light", "0–2", "32,697", ""], ["3 March 2006", "Manchester City", "City of Manchester Stadium", "1–2", "42,200", "Kyle"], ["13 August 2005", "Charlton Athletic", "Stadium of Light", "1–3", "34,446", "Gray"], ["10 September 2005", "Chelsea", "Stamford Bridge", "0–2", "41,969", ""], ["25 March 2006", "Blackburn Rovers", "Stadium of Light", "0–1", "29,593", ""], ["17 April 2006", "Newcastle United", "Stadium of Light", "1–4", "40,032", "Hoyte"], ["10 December 2005", "Charlton Athletic", "The Valley", "0–2", "26,065", ""], ["15 February 2006", "Blackburn Rovers", "Ewood Park", "0–2", "18,220", ""], ["23 August 2005", "Manchester City", "Stadium of Light", "1–2", "33,357", "Le Tallec"], ["23 October 2005", "Newcastle United", "St James' Park", "2–3", "52,302", "Lawrence, Elliott"], ["2 January 2006", "Fulham", "Craven Cottage", "1–2", "19,372", "Lawrence"], ["4 May 2006", "Fulham", "Stadium of Light", "2–1", "28,226", "Le Tallec, Brown"], ["17 September 2005", "West Bromwich Albion", "Stadium of Light", "1–1", "31,657", "Breen"]] | 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 more times did miller score than whitehead? | 0 | 128 | Answer: |
Table InputTable: [["Year", "Model", "Denomination", "Metal composition", "Dimensions"], ["1998", "80th anniversary of declaration of Independence, 1918–1998", "100 krooni", ".925 silver", "25 grams (0.88 oz)38 millimetres (1.5 in)"], ["1998", "80th anniversary of declaration of Independence, 1918–1998", "10 krooni", ".925 silver", "25 grams (0.88 oz)38 millimetres (1.5 in)"], ["2004", "The Flag of Estonia – 2004", "10 krooni", ".925 silver", "25 grams (0.88 oz)38 millimetres (1.5 in)"], ["1992", "Re-establishment of kroon, 28 August 1992", "100 krooni", ".900 silver", "23 grams (0.81 oz)36 millimetres (1.4 in)"], ["1992", "Barcelona Olympics", "10 krooni", ".925 silver", "25 grams (0.88 oz)38 millimetres (1.5 in)"], ["1992", "Re-establishment of Krooni currency", "10 krooni", ".925 silver", "25 grams (0.88 oz)38 millimetres (1.5 in)"], ["1996", "Atlanta Olympics, 100th anniversary of Modern Olympiad", "100 krooni", ".925 silver", "25 grams (0.88 oz)38 millimetres (1.5 in)"], ["2002", "370th anniversary of the founding of Tartu University", "10 krooni", ".925 silver", "25 grams (0.88 oz)38 millimetres (1.5 in)"]] | 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 metal composition has the least dimension? | .900 silver | 128 | Answer: |
Table InputTable: [["Rank", "Mountain Peak", "Nation", "Province", "Elevation", "Prominence", "Isolation"], ["4", "Cerro las Minas PB", "Honduras", "Lempira", "2849 m\\n9,347 ft", "2069 m\\n6,788 ft", "130 km\\n81 mi"], ["14", "Montaña San Ildefonso PB", "Honduras", "Cortés", "2242 m\\n7,356 ft", "1702 m\\n5,584 ft", "68 km\\n42 mi"], ["13", "Pico Bonito PB", "Honduras", "Atlántida", "2450 m\\n8,038 ft", "1710 m\\n5,610 ft", "152 km\\n95 mi"], ["3", "Montaña de Santa Bárbara PB", "Honduras", "Santa Bárbara", "2744 m\\n9,003 ft", "2084 m\\n6,837 ft", "74 km\\n46 mi"], ["8", "Montañas Peña Blanca High Point PB", "Guatemala", "Huehuetenango", "3518 m\\n11,542 ft", "1858 m\\n6,096 ft", "42 km\\n26 mi"], ["5", "Volcán de Agua PB", "Guatemala", "Escuintla\\nSacatepéquez", "3761 m\\n12,339 ft", "1981 m\\n6,499 ft", "16 km\\n10 mi"], ["12", "Volcán Atitlán PB", "Guatemala", "Sololá", "3537 m\\n11,604 ft", "1754 m\\n5,755 ft", "35 km\\n21 mi"], ["15", "Volcán San Cristóbal PB", "Nicaragua", "Chinandega", "1745 m\\n5,725 ft", "1665 m\\n5,463 ft", "134 km\\n83 mi"], ["9", "Volcán Acatenango PB", "Guatemala", "Chimaltenango\\nSacatepéquez", "3975 m\\n13,041 ft", "1835 m\\n6,020 ft", "126 km\\n78 mi"], ["7", "Volcán Irazú PB", "Costa Rica", "Cartago\\nSan José", "3402 m\\n11,161 ft", "1872 m\\n6,142 ft", "48 km\\n30 mi"], ["11", "Cerro Tacarcuna PB", "Panama", "Darién", "1875 m\\n6,152 ft", "1770 m\\n5,807 ft", "99 km\\n61 mi"], ["2", "Chirripó Grande PB", "Costa Rica", "Cartago\\nLimón\\nSan José", "3819 m\\n12,530 ft", "3726 m\\n12,224 ft", "864 km\\n537 mi"], ["6", "Alto Cuchumatanes PB", "Guatemala", "Huehuetenango", "3837 m\\n12,589 ft", "1877 m\\n6,158 ft", "65 km\\n40 mi"], ["1", "Volcán Tajumulco PB", "Guatemala", "San Marcos", "4220 m\\n13,845 ft", "3980 m\\n13,058 ft", "722 km\\n448 mi"], ["10", "Volcán San Miguel PB", "El Salvador", "San Miguel", "2131 m\\n6,991 ft", "1831 m\\n6,007 ft", "64 km\\n40 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:what are the total number of mountain peaks listed on this chart that are located in honduras? | 5 | 128 | Answer: |
Table InputTable: [["Location", "Reactor type", "Status", "Net\\nCapacity\\n(MWe)", "Gross\\nCapacity\\n(MWe)"], ["Ignalina-3", "RBMK-1500", "construction cancelled in 1988", "1,380", "1,500"], ["Kostroma-2", "RBMK-1500", "construction cancelled in 1980s", "1,380", "1,500"], ["Kostroma-1", "RBMK-1500", "construction cancelled in 1980s", "1,380", "1,500"], ["Kursk-6", "RBMK-1000", "construction cancelled in 1993", "925", "1,000"], ["Smolensk-4", "RBMK-1000", "construction cancelled in 1993", "925", "1,000"], ["Ignalina-4", "RBMK-1500", "plan cancelled in 1988", "1,380", "1,500"], ["Chernobyl-6", "RBMK-1000", "construction cancelled in 1988", "950", "1,000"], ["Chernobyl-5", "RBMK-1000", "construction cancelled in 1988", "950", "1,000"], ["Kursk-5", "MKER-1000", "construction begin was 1985, since then shelved", "925", "1,000"], ["Ignalina-1", "RBMK-1500", "shut down in 2004", "1,185", "1,300"], ["Ignalina-2", "RBMK-1500", "shut down in 2009", "1,185", "1,300"], ["Chernobyl-1", "RBMK-1000", "shut down in 1996", "740", "800"], ["Chernobyl-3", "RBMK-1000", "shut down in 2000", "925", "1,000"], ["Chernobyl-2", "RBMK-1000", "shut down in 1991", "925", "1,000"], ["Chernobyl-4", "RBMK-1000", "destroyed in the 1986 accident", "925", "1,000"], ["Kursk-3", "RBMK-1000", "operational until March 2014", "925", "1,000"], ["Leningrad-3", "RBMK-1000", "operational until June 2025", "925", "1,000"], ["Kursk-2", "RBMK-1000", "operational until 2024", "925", "1,000"], ["Kursk-4", "RBMK-1000", "operational until February 2016", "925", "1,000"], ["Leningrad-1", "RBMK-1000", "operational", "925", "1,000"], ["Leningrad-4", "RBMK-1000", "operational until August 2026", "925", "1,000"], ["Leningrad-2", "RBMK-1000", "operational until 2021", "925", "1,000"], ["Smolensk-3", "RBMK-1000", "operational until July 2023", "925", "1,000"], ["Smolensk-1", "RBMK-1000", "operational until December 2022", "925", "1,000"], ["Kursk-1", "RBMK-1000", "operational until 2021", "925", "1,000"], ["Smolensk-2", "RBMK-1000", "operational until July 2015", "925", "1,000"]] | 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:the construction of how many locations was cancelled in 1988? | 3 | 128 | Answer: |
Table InputTable: [["Event", "Class", "Gold", "Silver", "Bronze"], ["Super-G", "LW6/8\\ndetails", "Rolf Heinzmann\\nSwitzerland (SUI)", "Lionel Brun\\nFrance (FRA)", "Wolfgang Moosbrugger\\nAustria (AUT)"], ["Giant slalom", "LW6/8\\ndetails", "Rolf Heinzmann\\nSwitzerland (SUI)", "Lionel Brun\\nFrance (FRA)", "Frank Pfortmueller\\nGermany (GER)"], ["Slalom", "LW6/8\\ndetails", "Wolfgang Moosbrugger\\nAustria (AUT)", "Rolf Heinzmann\\nSwitzerland (SUI)", "Lionel Brun\\nFrance (FRA)"], ["Slalom", "LW3,5/7,9\\ndetails", "Gerd Schoenfelder\\nGermany (GER)", "Arno Hirschbuehl\\nAustria (AUT)", "Alexei Moshkine\\nRussia (RUS)"], ["Giant slalom", "LW11\\ndetails", "Harald Eder\\nAustria (AUT)", "Juergen Egle\\nAustria (AUT)", "Andreas Schiestl\\nAustria (AUT)"], ["Slalom", "LW4\\ndetails", "Hubert Mandl\\nAustria (AUT)", "Hans Burn\\nSwitzerland (SUI)", "Martin Falch\\nAustria (AUT)"], ["Downhill", "LW6/8\\ndetails", "Rolf Heinzmann\\nSwitzerland (SUI)", "Lionel Brun\\nFrance (FRA)", "Markus Pfefferle\\nGermany (GER)"], ["Slalom", "LW11\\ndetails", "Denis Barbet\\nFrance (FRA)", "Juergen Egle\\nAustria (AUT)", "Harald Eder\\nAustria (AUT)"], ["Slalom", "LW12\\ndetails", "Daniel Wesley\\nCanada (CAN)", "Hans Joerg Arnold\\nSwitzerland (SUI)", "Ludwig Wolf\\nGermany (GER)"], ["Giant slalom", "LW3,5/7,9\\ndetails", "Gerd Schoenfelder\\nGermany (GER)", "Romain Riboud\\nFrance (FRA)", "Arno Hirschbuehl\\nAustria (AUT)"], ["Giant slalom", "LW12\\ndetails", "Hans Joerg Arnold\\nSwitzerland (SUI)", "Sang Min Han\\nSouth Korea (KOR)", "Scott Patterson\\nCanada (CAN)"], ["Slalom", "LW2\\ndetails", "Michael Milton\\nAustralia (AUS)", "Monte Meier\\nUnited States (USA)", "Michael Hipp\\nGermany (GER)"], ["Giant slalom", "LW4\\ndetails", "Steven Bayley\\nNew Zealand (NZL)", "Hans Burn\\nSwitzerland (SUI)", "Robert Meusburger\\nAustria (AUT)"], ["Super-G", "LW11\\ndetails", "Fabrizio Zardini\\nItaly (ITA)", "Andreas Schiestl\\nAustria (AUT)", "Denis Barbet\\nFrance (FRA)"], ["Downhill", "LW11\\ndetails", "Harald Eder\\nAustria (AUT)", "Andreas Schiestl\\nAustria (AUT)", "Fabrizio Zardini\\nItaly (ITA)"], ["Slalom", "B1-2\\ndetails", "Eric Villalon\\nGuide: Pere Comet\\nSpain (ESP)", "Radomir Dudas\\nGuide: Juraj Mikulas\\nSlovakia (SVK)", "Stefan Kopcik\\nGuide: Branislav Mazgut\\nSlovakia (SVK)"], ["Super-G", "LW4\\ndetails", "Hubert Mandl\\nAustria (AUT)", "Josef Schoesswendter\\nAustria (AUT)", "Steven Bayley\\nNew Zealand (NZL)"], ["Giant slalom", "B1-2\\ndetails", "Eric Villalon\\nGuide: Pere Comet\\nSpain (ESP)", "Bart Bunting\\nGuide: Nathan Chivers\\nAustralia (AUS)", "Radomir Dudas\\nGuide: Juraj Mikulas\\nSlovakia (SVK)"]] | 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 medals won by germany? | 14 | 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"], ["Feb. 15, 1964", "Michigan State", "7–2", "Win", "Coliseum, Ann Arbor, MI"], ["March 12, 1964", "Michigan Tech", "4–3", "Win", "Coliseum, Ann Arbor, MI"], ["March 13, 1964", "Michigan Tech", "5–5", "Tie", "Coliseum, Ann Arbor, MI"], ["Feb. 14, 1964", "Michigan State", "2–0", "Win", "East Lansing, MI"], ["Jan. 24, 1964", "Michigan Tech", "6–2", "Win", "Coliseum, Ann Arbor, MI"], ["Jan. 25, 1964", "Michigan Tech", "5–3", "Win", "Coliseum, Ann Arbor, MI"], ["Jan. 31, 1964", "Colorado College", "7–0", "Win", "Coliseum, Ann Arbor, MI"], ["Feb. 22, 1964", "Minnesota", "8–2", "Win", "Coliseum, Ann Arbor, MI"], ["Jan. 17, 1964", "Loyola (Montreal)", "12–1", "Win", "Coliseum, Ann Arbor, MI"], ["Jan. 18, 1964", "Loyola (Montreal)", "14–2", "Win", "Coliseum, Ann Arbor, MI"], ["Feb. 29, 1964", "Michigan Tech", "4–3", "Win", "Houghton, MI"], ["Feb. 8, 1964", "Ohio State", "21–0", "Win", "Columbus, OH"], ["Feb. 1, 1964", "Colorado College", "12–4", "Win", "Coliseum, Ann Arbor, MI"], ["Feb. 21, 1964", "Minnesota", "6–3", "Win", "Coliseum, Ann Arbor, MI"], ["Dec. 14, 1963", "Toronto", "10–0", "Win", "Coliseum, Ann Arbor, MI"], ["Feb. 7, 1964", "Ohio", "14–0", "Win", "Athens, OH"], ["March 14, 1964", "Denver", "2–6", "Loss", "Coliseum, Ann Arbor, MI"], ["Jan. 10, 1964", "Minnesota", "5–1", "Win", "Minneapolis, MN"], ["Feb. 28, 1964", "Michigan Tech", "1–3", "Loss", "Houghton, MI"], ["Nov. 29, 1963", "Queen's", "9–5", "Win", "Coliseum, Ann Arbor, MI"], ["Nov. 30, 1963", "Queen's", "9–5", "Win", "Coliseum, Ann Arbor, MI"], ["Dec. 13, 1963", "Toronto", "3–5", "Loss", "Coliseum, Ann Arbor, MI"], ["March 20, 1964", "Providence", "3–2", "Win", "Denver, CO"], ["March 21, 1964", "Denver", "6–3", "Win", "Denver, CO"], ["Jan. 8, 1964", "Minn-Duluth", "7–2", "Win", "Duluth, MN"], ["Jan. 7, 1964", "Minn-Duluth", "8–4", "Win", "Duluth, MN"], ["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:which game in the ncaa tournament did the wolverines win by a larger margin? | March 21, 1964, Denver | 128 | Answer: |
Table InputTable: [["Name of place", "Number of counties", "Principal county", "Lower zip code", "Upper zip code"], ["Sadsbury Township", "1", "Crawford County", "", ""], ["Sadsbury Township", "1", "Chester County", "", ""], ["Sadsbury Township", "1", "Lancaster County", "", ""], ["Sadsburyville", "1", "Chester County", "19369", ""], ["Sadsbury Meeting House", "1", "Lancaster County", "", ""], ["Shamokin Township", "1", "Northumberland County", "", ""], ["Sharon Township", "1", "Potter County", "", ""], ["Shrewsbury Township", "1", "York County", "", ""], ["Salisbury Township", "1", "Lehigh County", "", ""], ["Sheshequin Township", "1", "Bradford County", "", ""], ["Scott Township", "1", "Lawrence County", "", ""], ["Salisbury Township", "1", "Lancaster County", "", ""], ["Scott Township", "1", "Wayne County", "", ""], ["Scott Township", "1", "Lackawanna County", "", ""], ["Shrewsbury Township", "1", "Sullivan County", "", ""], ["Seven Points", "1", "Northumberland County", "17801", ""], ["Silver Lake Township", "1", "Susquehanna County", "", ""], ["Shade Township", "1", "Somerset County", "", ""], ["Sergeant Township", "1", "McKean County", "", ""], ["Shirley Township", "1", "Huntingdon County", "", ""], ["Shirksville", "1", "Lebanon County", "", ""], ["Scarlan Hill", "1", "Cambria County", "", ""], ["Salford Township", "1", "Montgomery County", "", ""], ["Shepherd Hills", "1", "Lehigh County", "", ""], ["Scott Township", "1", "Columbia County", "", ""], ["Sheffield Township", "1", "Warren County", "", ""], ["Saville Township", "1", "Perry County", "", ""], ["Sandy Creek Township", "1", "Mercer County", "", ""], ["Schaefferstown", "1", "Lebanon County", "17088", ""], ["Silver Spring Township", "1", "Cumberland County", "", ""], ["Sandy Lake Township", "1", "Mercer County", "", ""], ["Scottdale", "1", "Westmoreland County", "15683", ""], ["Silverdale", "1", "Bucks County", "18962", ""], ["Shamokin", "1", "Northumberland County", "17872", ""], ["Simmonstown", "1", "Lancaster County", "17527", ""], ["Shamokin Dam", "1", "Snyder County", "17876", ""], ["Salem Township", "1", "Mercer County", "", ""], ["Sewickley Township", "1", "Westmoreland County", "", ""], ["Scott Township", "1", "Allegheny County", "15106", ""], ["Saltlick Township", "1", "Fayette County", "", ""], ["Sherersville", "1", "Lehigh County", "18104", ""], ["Scullton", "1", "Somerset County", "15557", ""], ["Shocks Mills", "1", "Lancaster County", "17547", ""], ["Shimerville", "1", "Lehigh County", "18049", ""], ["Shohola Township", "1", "Pike County", "", ""], ["Sandy Township", "1", "Clearfield County", "", ""], ["Shillington", "1", "Berks County", "19607", ""], ["Shiremanstown", "1", "Cumberland County", "17011", ""], ["Shingletown", "1", "Centre County", "16801", ""], ["Scarlets Mill", "1", "Berks County", "19508", ""], ["Selinsgrove", "1", "Snyder County", "17870", ""], ["Shaw Mines", "1", "Somerset County", "15552", ""], ["Sewickley Heights Township", "1", "Allegheny County", "", ""], ["Schweibinzville", "1", "Somerset County", "", ""], ["Salem Township", "1", "Wayne County", "", ""], ["Simpson", "1", "Lackawanna County", "18407", ""]] | 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 is sadsbury township listed? | 3 | 128 | Answer: |
Table InputTable: [["Period", "Live births per year", "Deaths per year", "Natural change per year", "CBR1", "CDR1", "NC1", "TFR1", "IMR1"], ["1975-1980", "18 000", "8 000", "10 000", "45.8", "19.6", "26.2", "6.67", "133.2"], ["1980-1985", "20 000", "8 000", "12 000", "42.7", "17.1", "25.6", "6.39", "117.1"], ["1970-1975", "16 000", "7 000", "8 000", "47.0", "22.0", "25.1", "6.67", "149.3"], ["1960-1965", "12 000", "6 000", "6 000", "48.5", "25.7", "22.8", "6.67", "174.1"], ["1965-1970", "13 000", "7 000", "7 000", "47.8", "24.1", "23.8", "6.67", "163.1"], ["1985-1990", "21 000", "8 000", "13 000", "40.4", "15.0", "25.3", "6.11", "104.0"], ["1990-1995", "19 000", "7 000", "12 000", "35.2", "12.5", "22.7", "5.27", "87.5"], ["2005-2010", "15 000", "5 000", "10 000", "21.5", "7.2", "14.4", "2.61", "44.4"], ["1950-1955", "9 000", "5 000", "4 000", "47.9", "27.1", "20.8", "6.67", "184.8"], ["1955-1960", "10 000", "6 000", "5 000", "49.0", "26.8", "22.3", "6.67", "181.4"], ["2000-2005", "15 000", "5 000", "11 000", "25.2", "7.9", "17.2", "3.30", "52.8"], ["1995-2000", "16 000", "5 000", "11 000", "29.2", "9.9", "19.3", "4.13", "69.7"]] | 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:how many deaths occurred per year on average in 1975-1980? | 8000 | 128 | Answer: |
Table InputTable: [["Year", "Model", "Denomination", "Metal composition", "Dimensions"], ["1992", "Re-establishment of Krooni currency", "10 krooni", ".925 silver", "25 grams (0.88 oz)38 millimetres (1.5 in)"], ["1998", "80th anniversary of declaration of Independence, 1918–1998", "100 krooni", ".925 silver", "25 grams (0.88 oz)38 millimetres (1.5 in)"], ["1998", "80th anniversary of declaration of Independence, 1918–1998", "10 krooni", ".925 silver", "25 grams (0.88 oz)38 millimetres (1.5 in)"], ["1992", "Re-establishment of kroon, 28 August 1992", "100 krooni", ".900 silver", "23 grams (0.81 oz)36 millimetres (1.4 in)"], ["2004", "The Flag of Estonia – 2004", "10 krooni", ".925 silver", "25 grams (0.88 oz)38 millimetres (1.5 in)"], ["1996", "Atlanta Olympics, 100th anniversary of Modern Olympiad", "100 krooni", ".925 silver", "25 grams (0.88 oz)38 millimetres (1.5 in)"], ["2002", "370th anniversary of the founding of Tartu University", "10 krooni", ".925 silver", "25 grams (0.88 oz)38 millimetres (1.5 in)"], ["1992", "Barcelona Olympics", "10 krooni", ".925 silver", "25 grams (0.88 oz)38 millimetres (1.5 in)"]] | 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 rwhat year coin was smaller than the rest | 1992 | 128 | Answer: |
Table InputTable: [["Rank", "Player", "County", "Tally", "Total", "Opposition"], ["9", "Francis Forde", "Galway", "1–5", "8", "New York"], ["1", "Francis Forde", "Galway", "2–8", "14", "Roscommon"], ["6", "Seán McLoughlin", "Westmeath", "1–6", "9", "Carlow"], ["9", "John Troy", "Offaly", "2–2", "8", "Laois"], ["3", "Gary Kirby", "Limerick", "0–10", "10", "Tipperary"], ["9", "John Byrne", "Carlow", "2–2", "8", "Westmeath"], ["6", "David Martin", "Meath", "1–6", "9", "Offaly"], ["6", "Gary Kirby", "Limerick", "0–9", "9", "Antrim"], ["9", "Paul Flynn", "Waterford", "1–5", "8", "Tipperary"], ["3", "Kevin Broderick", "Galway", "3–1", "10", "New York"], ["3", "Gary Kirby", "Limerick", "1–7", "10", "Tipperary"], ["9", "John Leahy", "Tipperary", "2–2", "8", "Kerry"], ["9", "Tom Dempsey", "Wexford", "1–5", "8", "Offaly"], ["2", "Niall English", "Carlow", "1–9", "12", "Westmeath"]] | 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 player ranked immediately after francis forde? | Niall English | 128 | Answer: |
Table InputTable: [["Year", "Chassis", "Engine", "Start", "Finish", "Team"], ["2009", "Dallara", "Honda", "1", "1", "Team Penske"], ["2006", "Dallara", "Honda", "2", "25", "Team Penske"], ["2008", "Dallara", "Honda", "4", "4", "Team Penske"], ["2011", "Dallara", "Honda", "16", "17", "Team Penske"], ["2007", "Dallara", "Honda", "1", "3", "Team Penske"], ["2010", "Dallara", "Honda", "1", "9", "Team Penske"], ["2005", "Dallara", "Toyota", "5", "9", "Team Penske"], ["2013", "Dallara", "Chevrolet", "8", "6", "Team Penske"], ["2012", "Dallara", "Chevrolet", "6", "10", "Team Penske"], ["2003", "Dallara", "Toyota", "1", "2", "Team Penske"], ["2004", "Dallara", "Toyota", "8", "9", "Team Penske"], ["2002", "Dallara", "Chevrolet", "13", "1", "Team Penske"], ["2001", "Dallara", "Oldsmobile", "11", "1", "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:when was the last year team penske finished first? | 2009 | 128 | Answer: |
Table InputTable: [["Year", "Competition", "Venue", "Position", "Event", "Notes"], ["2006", "Commonwealth Games", "Melbourne, Australia", "7th", "Shot put", "18.44 m"], ["2004", "African Championships", "Brazzaville, Republic of the Congo", "2nd", "Discus throw", "63.50 m"], ["2006", "Commonwealth Games", "Melbourne, Australia", "4th", "Discus throw", "60.99 m"], ["2008", "African Championships", "Addis Ababa, Ethiopia", "2nd", "Discus throw", "56.98 m"], ["2000", "World Junior Championships", "Santiago, Chile", "1st", "Discus throw", "59.51 m"], ["2007", "All-Africa Games", "Algiers, Algeria", "3rd", "Discus throw", "57.79 m"], ["2003", "All-Africa Games", "Abuja, Nigeria", "2nd", "Discus throw", "62.86 m"], ["2003", "All-Africa Games", "Abuja, Nigeria", "5th", "Shot put", "17.76 m"], ["2004", "Olympic Games", "Athens, Greece", "8th", "Discus throw", "62.58 m"]] | 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 did the competitions go on? | 8 years | 128 | Answer: |
Table InputTable: [["Year", "Title", "Genre", "Publisher", "Notes"], ["1909", "The ShortStop", "Baseball", "A. C. McClurg", ""], ["1920", "The Redheaded Outfield and other Baseball Stories", "Baseball", "Harper & Brothers", ""], ["1911", "The Young Pitcher", "Baseball", "Harper & Brothers", ""], ["1938", "Raiders of Spanish Peaks", "Western", "Whitman Publishing", ""], ["1937", "West of the Pecos", "Western", "Whitman Publishing", ""], ["1909", "The Last Trail", "Western", "Outing Publishing", "Sequel to Spirit of the Border"], ["1908", "The Last of the Plainsmen", "Western", "Outing Publishing", "Inspired by Charles \"Buffalo\" Jones"], ["1935", "The Trail Driver", "Western", "Whitman Publishing", ""], ["1994", "George Washington, Frontiersman", "Historical", "Forge Books", ""], ["1975", "Zane Grey's Greatest Indian Stories", "Western", "Dorchester Publishing", "Includes original ending to The Vanishing American (1925)"], ["1982", "Angler's Eldorado: Zane Grey in New Zealand", "Fishing", "Walter J. Black", "Partial reprint of 1926 edition (first 10 chapters, plus additional content)"], ["1937", "An American Angler in Australia", "Fishing", "Whitman Publishing", ""], ["1930", "The Shepherd of Guadaloupe", "Western", "Harper & Brothers", ""], ["1963", "Boulder Dam", "Historical", "HarperCollins", ""], ["1958", "Arizona Clan", "Western", "Harper & Brothers", ""], ["1903", "Betty Zane", "Historical", "Charles Francis Press", ""], ["1925", "The Thundering Herd", "Western", "Harper & Brothers", ""], ["1943", "Omnibus", "Western", "Harper & Brothers", ""], ["1936", "The Lost Wagon Train", "Western", "Harper & Brothers", ""], ["1933", "The Hash Knife Outfit", "Western", "Harper & Brothers", "Sequel to The Drift Fence"], ["1911", "The Young Lion Hunter", "Western", "Harper & Brothers", ""], ["1923", "Wanderer of the Wasteland", "Western", "Harper & Brothers", ""], ["1930", "The Wolf Tracker", "Western", "Harper & Brothers", ""], ["1932", "Arizona Ames", "Western", "Harper & Brothers", ""], ["1939", "Knights of the Range", "Western", "Harper & Brothers", ""], ["1906", "Spirit of the Border", "Historical", "A. L. Burt & Company", "Sequel to Betty Zane"], ["1954", "Lost Pueblo", "Western", "Harper & Brothers", ""], ["1910", "The Young Forester", "Western", "Harper & Brothers", ""], ["1933", "The Drift Fence", "Western", "Harper & Brothers", ""], ["1925", "The Vanishing American", "Western", "Harper & Brothers", ""], ["1960", "The Ranger and other Stories", "Western", "Harper & Row", ""], ["1940", "Twin Sombreros", "Western", "Harper & Brothers", "Sequel to Knights of the Range"], ["1926", "Tales of the Angler's Eldorado, New Zealand", "Fishing", "Harper & Brothers", ""], ["1925", "Tales of Fishing Virgin Seas", "Fishing", "Harper & Brothers", ""], ["1928", "Tales of Fresh Water Fishing", "Fishing", "Harper & Brothers", ""], ["1919", "Tales of Fishes", "Fishing", "Harper & Brothers", ""], ["1912", "Riders of the Purple Sage", "Western", "Harper & Brothers", ""], ["1996", "Last of the Duanes", "Western", "Gunsmoke Westerns", "Unabridged version of The Lone Star Ranger (1915)"], ["1931", "Book of Camps and Trails", "Adventure", "Harper & Brothers", "Partial re-print of Tales of Lonely Trails"], ["1939", "Western Union", "Western", "Harper & Brothers", ""], ["1928", "Don, the Story of a Lion Dog", "Western", "Harper & Brothers", ""]] | 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 year was a baseball book published after, "the shortstop" was published? | 1911 | 128 | Answer: |
Table InputTable: [["No. in\\nseason", "No. in\\nseries", "Title", "Canadian airdate", "US airdate", "Production code"], ["23–24", "340–341", "\"Unbelievable\"", "March 11, 2014", "March 11, 2014", "1323 & 1324"], ["29", "346", "\"Sparks Will Fly\" Part One", "April 15, 2014", "April 15, 2014", "1329"], ["12", "329", "\"Everything You've Done Wrong\"", "October 24, 2013", "October 24, 2013", "1312"], ["22", "339", "\"Basket Case\"", "March 4, 2014", "March 4, 2014", "1322"], ["13", "330", "\"Who Do You Think You Are\"", "October 31, 2013", "October 31, 2013", "1313"], ["14", "331", "\"Barely Breathing\"", "November 7, 2013", "November 7, 2013", "1314"], ["8", "325", "\"Young Forever\"", "August 22, 2013", "August 22, 2013", "1308"], ["39", "356", "\"Thunderstruck\" Part One", "July 29, 2014", "July 29, 2014", "1339"], ["26", "343", "\"Close to Me\"", "March 25, 2014", "March 25, 2014", "1326"], ["16", "333", "\"Spiderwebs\"", "November 21, 2013", "November 21, 2013", "1316"], ["30", "347", "\"Sparks Will Fly\" Part Two", "April 22, 2014", "April 22, 2014", "1330"], ["4", "321", "\"My Own Worst Enemy\"", "July 25, 2013", "July 25, 2013", "1304"], ["9", "326", "\"This Is How We Do It\"", "October 3, 2013", "October 3, 2013", "1309"], ["25", "342", "\"What It's Like\"", "March 18, 2014", "March 18, 2014", "1325"], ["3", "320", "\"All I Wanna Do\"", "July 18, 2013", "July 18, 2013", "1303"], ["10", "327", "\"You Got Me\"", "October 10, 2013", "October 10, 2013", "1310"], ["33", "350", "\"How Bizarre\"", "June 17, 2014", "June 17, 2014", "1333"], ["11", "328", "\"You Oughta Know\"", "October 17, 2013", "October 17, 2013", "1311"], ["19", "336", "\"Dig Me Out\"", "February 11, 2014", "February 11, 2014", "1319"], ["34", "351", "\"My Hero\"", "June 24, 2014", "June 24, 2014", "1334"], ["27", "344", "\"Army of Me\"", "April 1, 2014", "April 1, 2014", "1327"], ["15", "332", "\"Black Or White\"", "November 14, 2013", "November 14, 2013", "1315"], ["17", "334", "\"The World I Know\"", "January 28, 2014", "January 28, 2014", "1317"], ["28", "345", "\"Everything Is Everything\"", "April 8, 2014", "April 8, 2014", "1328"], ["7", "324", "\"Honey\"", "August 15, 2013", "August 15, 2013", "1307"], ["5", "322", "\"About A Girl\"", "August 1, 2013", "August 1, 2013", "1305"], ["40", "357", "\"Thundestruck\" Part Two", "July 29, 2014", "July 29, 2014", "1340"], ["6", "323", "\"Cannonball\"", "August 8, 2013", "August 8, 2013", "1306"], ["1–2", "318–319", "\"Summertime\"", "July 11, 2013", "July 11, 2013", "1301 & 1302"]] | 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 episodes aired in season 13? | 40 | 128 | Answer: |
Table InputTable: [["Round", "Round", "Race", "Date", "Pole Position", "Fastest Lap", "Winning Club", "Winning Team", "Report"], ["5", "R1", "Vallelunga", "November 2", "Liverpool F.C.", "Beijing Guoan", "Beijing Guoan", "Zakspeed", "Report"], ["6", "R2", "Jerez", "November 23", "", "Beijing Guoan", "Borussia Dortmund", "Zakspeed", "Report"], ["3", "R2", "Zolder", "October 5", "", "Atlético Madrid", "Beijing Guoan", "Zakspeed", "Report"], ["3", "R1", "Zolder", "October 5", "Borussia Dortmund", "Liverpool F.C.", "Liverpool F.C.", "Hitech Junior Team", "Report"], ["6", "R1", "Jerez", "November 23", "Liverpool F.C.", "R.S.C. Anderlecht", "A.C. Milan", "Scuderia Playteam", "Report"], ["4", "R2", "Estoril", "October 19", "", "Borussia Dortmund", "Al Ain", "Azerti Motorsport", "Report"], ["2", "R1", "Nürburgring", "September 21", "A.C. Milan", "PSV Eindhoven", "A.C. Milan", "Scuderia Playteam", "Report"], ["2", "R2", "Nürburgring", "September 21", "", "SC Corinthians", "PSV Eindhoven", "Azerti Motorsport", "Report"], ["5", "R2", "Vallelunga", "November 2", "", "Atlético Madrid", "F.C. Porto", "Hitech Junior Team", "Report"], ["1", "R2", "Donington Park", "August 31", "", "PSV Eindhoven", "Sevilla FC", "GTA Motor Competición", "Report"], ["4", "R1", "Estoril", "October 19", "A.S. Roma", "Atlético Madrid", "Liverpool F.C.", "Hitech Junior Team", "Report"], ["1", "R1", "Donington Park", "August 31", "Beijing Guoan", "Beijing Guoan", "Beijing Guoan", "Zakspeed", "Report"]] | 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 zakspeed the winning team? | 4 | 128 | Answer: |
Table InputTable: [["Season", "Conference", "Head Coach", "Total Wins", "Total Losses", "Total Ties", "Conference Wins", "Conference Losses", "Conference Ties", "Conference Standing", "Postseason Result"], ["2001", "Southern", "Ellis Johnson", "3", "7", "0", "2", "6", "0", "7", "—"], ["1992", "Southern", "Charlie Taaffe", "11", "2", "0", "6", "1", "0", "1", "Quarterfinals"], ["2002", "Southern", "Ellis Johnson", "3", "9", "—", "1", "7", "—", "9", "—"], ["2003", "Southern", "Ellis Johnson", "6", "6", "—", "4", "4", "—", "4", "—"], ["1988", "Southern", "Charlie Taaffe", "8", "4", "0", "5", "2", "0", "3", "First Round"], ["1950", "Southern", "J. Quinn Decker", "4", "6", "0", "2", "3", "0", "11", "—"], ["Totals:\\n105 Seasons", "2 Conferences", "23 Head Coaches", "Total\\nWins\\n473", "Total\\nLosses\\n536", "Total\\nTies\\n32", "239 Conference Wins\\n55 SIAA\\n184 SoCon", "379 Conference Losses\\n58 SIAA\\n321 SoCon", "13 Conference Ties\\n8 SIAA\\n5 SoCon", "Regular Season\\nChampions\\n2 times", "1–0 Bowl Record\\n1–3 Playoff Record"], ["1930", "Southern Intercollegiate", "Johnny Floyd", "4", "5", "2", "3", "0", "1", "—", "—"], ["1951", "Southern", "J. Quinn Decker", "4", "6", "0", "1", "3", "0", "14", "—"], ["1990", "Southern", "Charlie Taaffe", "7", "5", "0", "4", "3", "0", "3", "First Round"], ["1949", "Southern", "J. Quinn Decker", "4", "5", "0", "2", "2", "0", "7", "—"], ["1909", "Southern Intercollegiate", "Sam Costen", "4", "3", "2", "0", "1", "1", "—", "—"], ["1948", "Southern", "J. Quinn Decker", "2", "7", "0", "0", "5", "0", "16", "—"], ["1910", "Southern Intercollegiate", "Sam Costen", "3", "4", "0", "1", "3", "0", "—", "—"], ["1946", "Southern", "J. Quinn Decker", "3", "5", "0", "1", "5", "0", "15", "—"], ["1947", "Southern", "J. Quinn Decker", "3", "5", "0", "1", "4", "0", "12", "—"], ["1931", "Southern Intercollegiate", "Johnny Floyd", "5", "4", "1", "4", "1", "0", "—", "—"], ["1965", "Southern", "Eddie Teague", "2", "8", "0", "2", "6", "0", "8", "—"], ["1958", "Southern", "Eddie Teague", "4", "6", "0", "2", "3", "0", "7", "—"], ["1963", "Southern", "Eddie Teague", "4", "6", "0", "2", "4", "0", "7", "—"], ["1964", "Southern", "Eddie Teague", "4", "6", "0", "4", "3", "0", "4", "—"], ["1908", "Southern Intercollegiate", "Ralph Foster", "4", "1", "1", "—", "—", "—", "—", "—"], ["1952", "Southern", "J. Quinn Decker", "3", "5", "1", "1", "3", "1", "13", "—"], ["1911", "Southern Intercollegiate", "L. S. LeTellier", "5", "2", "2", "1", "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:who became head coach after ellis johnson? | John Zernhelt | 128 | Answer: |
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