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14,886
student_1
spider:train_spider.json:4061
Which are the first and last names of the students taught by MARROTTE KIRK?
SELECT T1.firstname , T1.lastname FROM list AS T1 JOIN teachers AS T2 ON T1.classroom = T2.classroom WHERE T2.firstname = "MARROTTE" AND T2.lastname = "KIRK"
[ "Which", "are", "the", "first", "and", "last", "names", "of", "the", "students", "taught", "by", "MARROTTE", "KIRK", "?" ]
[ { "id": 0, "type": "column", "value": "firstname" }, { "id": 4, "type": "column", "value": "classroom" }, { "id": 1, "type": "column", "value": "lastname" }, { "id": 3, "type": "table", "value": "teachers" }, { "id": 5, "type": "column", "value": "MARROTTE" }, { "id": 2, "type": "table", "value": "list" }, { "id": 6, "type": "column", "value": "KIRK" } ]
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[ "O", "O", "B-TABLE", "I-TABLE", "O", "B-TABLE", "B-COLUMN", "O", "O", "O", "O", "O", "B-COLUMN", "B-COLUMN", "O" ]
14,887
bike_1
spider:train_spider.json:160
Which days had a minimum dew point smaller than any day in zip code 94107, and in which zip codes were those measurements taken?
SELECT date , zip_code FROM weather WHERE min_dew_point_f < (SELECT min(min_dew_point_f) FROM weather WHERE zip_code = 94107)
[ "Which", "days", "had", "a", "minimum", "dew", "point", "smaller", "than", "any", "day", "in", "zip", "code", "94107", ",", "and", "in", "which", "zip", "codes", "were", "those", "measurements", "taken", "?" ]
[ { "id": 3, "type": "column", "value": "min_dew_point_f" }, { "id": 2, "type": "column", "value": "zip_code" }, { "id": 0, "type": "table", "value": "weather" }, { "id": 4, "type": "value", "value": "94107" }, { "id": 1, "type": "column", "value": "date" } ]
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[ "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "O", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "B-VALUE", "O", "O", "O", "O", "O", "O", "B-TABLE", "I-TABLE", "O", "O", "O" ]
14,888
simpson_episodes
bird:train.json:4232
What is the total number of awards won by The simpson 20s: Season 20?
SELECT COUNT(award_id) FROM Award WHERE result = 'Winner';
[ "What", "is", "the", "total", "number", "of", "awards", "won", "by", "The", "simpson", "20s", ":", "Season", "20", "?" ]
[ { "id": 3, "type": "column", "value": "award_id" }, { "id": 1, "type": "column", "value": "result" }, { "id": 2, "type": "value", "value": "Winner" }, { "id": 0, "type": "table", "value": "award" } ]
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[ "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "O", "O", "O" ]
14,889
food_inspection
bird:train.json:8820
Among the top 5 owners with highest number of establishments, which owner has the highest number of high risk violations? Give the name of the owner.
SELECT T4.owner_name FROM violations AS T3 INNER JOIN businesses AS T4 ON T3.business_id = T4.business_id INNER JOIN ( SELECT T2.owner_name FROM violations AS T1 INNER JOIN businesses AS T2 ON T1.business_id = T2.business_id GROUP BY T2.owner_name ORDER BY COUNT(T1.business_id) DESC LIMIT 5 ) AS T5 ON T4.owner_name = T5.owner_name WHERE T3.risk_category = 'High Risk' GROUP BY T4.owner_name ORDER BY COUNT(T3.risk_category) DESC LIMIT 1
[ "Among", "the", "top", "5", "owners", "with", "highest", "number", "of", "establishments", ",", "which", "owner", "has", "the", "highest", "number", "of", "high", "risk", "violations", "?", "Give", "the", "name", "of", "the", "owner", "." ]
[ { "id": 1, "type": "column", "value": "risk_category" }, { "id": 5, "type": "column", "value": "business_id" }, { "id": 0, "type": "column", "value": "owner_name" }, { "id": 3, "type": "table", "value": "violations" }, { "id": 4, "type": "table", "value": "businesses" }, { "id": 2, "type": "value", "value": "High Risk" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "O", "O", "O", "B-VALUE", "I-VALUE", "B-TABLE", "O", "O", "O", "O", "O", "O", "O", "O" ]
14,890
student_loan
bird:train.json:4430
Count the number of male students who belong to foreign legion.
SELECT COUNT(T1.name) FROM male AS T1 INNER JOIN enlist AS T2 ON T1.name = T2.name WHERE T2.organ = 'foreign_legion'
[ "Count", "the", "number", "of", "male", "students", "who", "belong", "to", "foreign", "legion", "." ]
[ { "id": 3, "type": "value", "value": "foreign_legion" }, { "id": 1, "type": "table", "value": "enlist" }, { "id": 2, "type": "column", "value": "organ" }, { "id": 0, "type": "table", "value": "male" }, { "id": 4, "type": "column", "value": "name" } ]
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[ "O", "O", "B-COLUMN", "O", "B-TABLE", "O", "O", "O", "O", "B-COLUMN", "B-VALUE", "O" ]
14,891
match_season
spider:train_spider.json:1083
Who are the different players, what season do they play in, and what is the name of the team they are on?
SELECT T1.Season , T1.Player , T2.Name FROM match_season AS T1 JOIN team AS T2 ON T1.Team = T2.Team_id
[ "Who", "are", "the", "different", "players", ",", "what", "season", "do", "they", "play", "in", ",", "and", "what", "is", "the", "name", "of", "the", "team", "they", "are", "on", "?" ]
[ { "id": 3, "type": "table", "value": "match_season" }, { "id": 6, "type": "column", "value": "team_id" }, { "id": 0, "type": "column", "value": "season" }, { "id": 1, "type": "column", "value": "player" }, { "id": 2, "type": "column", "value": "name" }, { "id": 4, "type": "table", "value": "team" }, { "id": 5, "type": "column", "value": "team" } ]
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[ "O", "O", "O", "O", "B-COLUMN", "O", "B-TABLE", "B-COLUMN", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "B-COLUMN", "O", "O", "O", "O" ]
14,892
customer_complaints
spider:train_spider.json:5812
Which state has the most customers?
SELECT state FROM customers GROUP BY state ORDER BY count(*) LIMIT 1
[ "Which", "state", "has", "the", "most", "customers", "?" ]
[ { "id": 0, "type": "table", "value": "customers" }, { "id": 1, "type": "column", "value": "state" } ]
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[ "O", "B-COLUMN", "O", "O", "O", "B-TABLE", "O" ]
14,893
apartment_rentals
spider:train_spider.json:1220
Show the apartment numbers, start dates, and end dates of all the apartment bookings.
SELECT T2.apt_number , T1.booking_start_date , T1.booking_start_date FROM Apartment_Bookings AS T1 JOIN Apartments AS T2 ON T1.apt_id = T2.apt_id
[ "Show", "the", "apartment", "numbers", ",", "start", "dates", ",", "and", "end", "dates", "of", "all", "the", "apartment", "bookings", "." ]
[ { "id": 1, "type": "column", "value": "booking_start_date" }, { "id": 2, "type": "table", "value": "apartment_bookings" }, { "id": 0, "type": "column", "value": "apt_number" }, { "id": 3, "type": "table", "value": "apartments" }, { "id": 4, "type": "column", "value": "apt_id" } ]
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[ "O", "O", "B-TABLE", "B-COLUMN", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "I-TABLE", "O" ]
14,894
chicago_crime
bird:train.json:8592
How many neighborhoods are there in the community area of Lincoln Square?
SELECT COUNT(T3.community_area_no) FROM ( SELECT T1.community_area_no FROM Community_Area AS T1 INNER JOIN Neighborhood AS T2 ON T1.community_area_no = T2.community_area_no WHERE community_area_name = 'Lincoln Square' GROUP BY T1.community_area_no ) T3
[ "How", "many", "neighborhoods", "are", "there", "in", "the", "community", "area", "of", "Lincoln", "Square", "?" ]
[ { "id": 3, "type": "column", "value": "community_area_name" }, { "id": 0, "type": "column", "value": "community_area_no" }, { "id": 1, "type": "table", "value": "community_area" }, { "id": 4, "type": "value", "value": "Lincoln Square" }, { "id": 2, "type": "table", "value": "neighborhood" } ]
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[ "O", "O", "B-TABLE", "O", "O", "O", "O", "B-TABLE", "I-TABLE", "O", "B-VALUE", "I-VALUE", "O" ]
14,895
legislator
bird:train.json:4806
How many legislators have not been registered in Federal Election Commission data?
SELECT COUNT(*) FROM current WHERE fec_id IS NULL OR fec_id = ''
[ "How", "many", "legislators", "have", "not", "been", "registered", "in", "Federal", "Election", "Commission", "data", "?" ]
[ { "id": 0, "type": "table", "value": "current" }, { "id": 1, "type": "column", "value": "fec_id" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O" ]
14,896
pilot_1
bird:test.json:1107
How many different places have some plane?
SELECT count(DISTINCT LOCATION) FROM hangar
[ "How", "many", "different", "places", "have", "some", "plane", "?" ]
[ { "id": 1, "type": "column", "value": "location" }, { "id": 0, "type": "table", "value": "hangar" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "O" ]
14,897
icfp_1
spider:train_spider.json:2887
Find the number of papers published by authors from the institution "Tokohu University".
SELECT count(DISTINCT t1.title) FROM papers AS t1 JOIN authorship AS t2 ON t1.paperid = t2.paperid JOIN inst AS t3 ON t2.instid = t3.instid WHERE t3.name = "Tokohu University"
[ "Find", "the", "number", "of", "papers", "published", "by", "authors", "from", "the", "institution", "\"", "Tokohu", "University", "\"", "." ]
[ { "id": 2, "type": "column", "value": "Tokohu University" }, { "id": 5, "type": "table", "value": "authorship" }, { "id": 7, "type": "column", "value": "paperid" }, { "id": 4, "type": "table", "value": "papers" }, { "id": 6, "type": "column", "value": "instid" }, { "id": 3, "type": "column", "value": "title" }, { "id": 0, "type": "table", "value": "inst" }, { "id": 1, "type": "column", "value": "name" } ]
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[ "B-COLUMN", "O", "B-COLUMN", "O", "B-TABLE", "O", "O", "B-TABLE", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O" ]
14,898
medicine_enzyme_interaction
spider:train_spider.json:956
What is the type of interaction for the enzyme named 'ALA synthase' and the medicine named 'Aripiprazole'?
SELECT T1.interaction_type FROM medicine_enzyme_interaction AS T1 JOIN medicine AS T2 ON T1.medicine_id = T2.id JOIN enzyme AS T3 ON T1.enzyme_id = T3.id WHERE T3.name = 'ALA synthase' AND T2.name = 'Aripiprazole'
[ "What", "is", "the", "type", "of", "interaction", "for", "the", "enzyme", "named", "'", "ALA", "synthase", "'", "and", "the", "medicine", "named", "'", "Aripiprazole", "'", "?" ]
[ { "id": 2, "type": "table", "value": "medicine_enzyme_interaction" }, { "id": 0, "type": "column", "value": "interaction_type" }, { "id": 7, "type": "value", "value": "ALA synthase" }, { "id": 8, "type": "value", "value": "Aripiprazole" }, { "id": 9, "type": "column", "value": "medicine_id" }, { "id": 4, "type": "column", "value": "enzyme_id" }, { "id": 3, "type": "table", "value": "medicine" }, { "id": 1, "type": "table", "value": "enzyme" }, { "id": 6, "type": "column", "value": "name" }, { "id": 5, "type": "column", "value": "id" } ]
[ { "entity_id": 0, "token_idxs": [ 5 ] }, { "entity_id": 1, "token_idxs": [ 8 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 16 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [ 9 ] }, { "entity_id": 7, "token_idxs": [ 11, 12 ] }, { "entity_id": 8, "token_idxs": [ 19 ] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "B-COLUMN", "O", "B-VALUE", "I-VALUE", "O", "O", "O", "B-TABLE", "O", "O", "B-VALUE", "O", "O" ]
14,899
store_product
spider:train_spider.json:4934
Find all the product whose name contains the word "Scanner".
SELECT product FROM product WHERE product LIKE "%Scanner%"
[ "Find", "all", "the", "product", "whose", "name", "contains", "the", "word", "\"", "Scanner", "\"", "." ]
[ { "id": 2, "type": "column", "value": "%Scanner%" }, { "id": 0, "type": "table", "value": "product" }, { "id": 1, "type": "column", "value": "product" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 3 ] }, { "entity_id": 2, "token_idxs": [ 10 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-COLUMN", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "O" ]
14,900
beer_factory
bird:train.json:5319
List the brand IDs of the beers whose star rating is more than 3.
SELECT BrandID FROM rootbeerreview WHERE StarRating > 3
[ "List", "the", "brand", "IDs", "of", "the", "beers", "whose", "star", "rating", "is", "more", "than", "3", "." ]
[ { "id": 0, "type": "table", "value": "rootbeerreview" }, { "id": 2, "type": "column", "value": "starrating" }, { "id": 1, "type": "column", "value": "brandid" }, { "id": 3, "type": "value", "value": "3" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 2, 3 ] }, { "entity_id": 2, "token_idxs": [ 8, 9 ] }, { "entity_id": 3, "token_idxs": [ 13 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "O", "B-VALUE", "O" ]
14,901
works_cycles
bird:train.json:7119
What are the company that Adventure Works deal with that have poor credit rating? Please provide their business number.
SELECT BusinessEntityID FROM Vendor WHERE CreditRating = ( SELECT CreditRating FROM Vendor ORDER BY CreditRating DESC LIMIT 1 )
[ "What", "are", "the", "company", "that", "Adventure", "Works", "deal", "with", "that", "have", "poor", "credit", "rating", "?", "Please", "provide", "their", "business", "number", "." ]
[ { "id": 1, "type": "column", "value": "businessentityid" }, { "id": 2, "type": "column", "value": "creditrating" }, { "id": 0, "type": "table", "value": "vendor" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 18 ] }, { "entity_id": 2, "token_idxs": [ 12, 13 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "O", "O", "B-COLUMN", "O", "O" ]
14,902
superstore
bird:train.json:2421
Calculate the difference between the total sales in the East superstore and the total sales in the West superstore.
SELECT SUM(T1.Sales) - SUM(T2.Sales) AS difference FROM east_superstore AS T1 INNER JOIN west_superstore AS T2 ON T1.`Customer ID` = T2.`Customer ID`
[ "Calculate", "the", "difference", "between", "the", "total", "sales", "in", "the", "East", "superstore", "and", "the", "total", "sales", "in", "the", "West", "superstore", "." ]
[ { "id": 0, "type": "table", "value": "east_superstore" }, { "id": 1, "type": "table", "value": "west_superstore" }, { "id": 2, "type": "column", "value": "Customer ID" }, { "id": 3, "type": "column", "value": "sales" } ]
[ { "entity_id": 0, "token_idxs": [ 9, 10 ] }, { "entity_id": 1, "token_idxs": [ 17, 18 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 14 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "I-TABLE", "O", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "I-TABLE", "O" ]
14,903
restaurant
bird:train.json:1750
How many of the cities are in a Bay Area?
SELECT COUNT(city) FROM geographic WHERE region = 'bay area'
[ "How", "many", "of", "the", "cities", "are", "in", "a", "Bay", "Area", "?" ]
[ { "id": 0, "type": "table", "value": "geographic" }, { "id": 2, "type": "value", "value": "bay area" }, { "id": 1, "type": "column", "value": "region" }, { "id": 3, "type": "column", "value": "city" } ]
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[ "O", "O", "O", "O", "B-COLUMN", "B-COLUMN", "I-COLUMN", "O", "B-VALUE", "I-VALUE", "O" ]
14,904
flight_1
spider:train_spider.json:382
What is the number of flights?
SELECT count(*) FROM Flight
[ "What", "is", "the", "number", "of", "flights", "?" ]
[ { "id": 0, "type": "table", "value": "flight" } ]
[ { "entity_id": 0, "token_idxs": [ 5 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "B-TABLE", "O" ]
14,905
game_injury
spider:train_spider.json:1292
For each injury accident, find the date of the game and the name of the injured player in the game, and sort the results in descending order of game season.
SELECT T1.date , T2.player FROM game AS T1 JOIN injury_accident AS T2 ON T1.id = T2.game_id ORDER BY T1.season DESC
[ "For", "each", "injury", "accident", ",", "find", "the", "date", "of", "the", "game", "and", "the", "name", "of", "the", "injured", "player", "in", "the", "game", ",", "and", "sort", "the", "results", "in", "descending", "order", "of", "game", "season", "." ]
[ { "id": 3, "type": "table", "value": "injury_accident" }, { "id": 6, "type": "column", "value": "game_id" }, { "id": 1, "type": "column", "value": "player" }, { "id": 4, "type": "column", "value": "season" }, { "id": 0, "type": "column", "value": "date" }, { "id": 2, "type": "table", "value": "game" }, { "id": 5, "type": "column", "value": "id" } ]
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[ "O", "O", "B-TABLE", "I-TABLE", "O", "B-COLUMN", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "B-COLUMN", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O" ]
14,906
planet_1
bird:test.json:1879
List package number and weight of top 3 lightest packages.
SELECT PackageNumber , Weight FROM PACKAGE ORDER BY Weight ASC LIMIT 3;
[ "List", "package", "number", "and", "weight", "of", "top", "3", "lightest", "packages", "." ]
[ { "id": 1, "type": "column", "value": "packagenumber" }, { "id": 0, "type": "table", "value": "package" }, { "id": 2, "type": "column", "value": "weight" } ]
[ { "entity_id": 0, "token_idxs": [ 1 ] }, { "entity_id": 1, "token_idxs": [ 2 ] }, { "entity_id": 2, "token_idxs": [ 4 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "B-TABLE", "B-COLUMN", "O", "B-COLUMN", "O", "O", "O", "O", "O", "O" ]
14,907
retail_complains
bird:train.json:338
Which region has the second most clients?
SELECT T2.division FROM client AS T1 INNER JOIN district AS T2 ON T1.district_id = T2.district_id GROUP BY T2.division ORDER BY COUNT(T2.division) DESC LIMIT 1, 1
[ "Which", "region", "has", "the", "second", "most", "clients", "?" ]
[ { "id": 3, "type": "column", "value": "district_id" }, { "id": 0, "type": "column", "value": "division" }, { "id": 2, "type": "table", "value": "district" }, { "id": 1, "type": "table", "value": "client" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 6 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "O", "B-TABLE", "O" ]
14,908
insurance_fnol
spider:train_spider.json:893
What are the customer phone numbers under the policy "Life Insurance"?
SELECT customer_phone FROM available_policies WHERE policy_type_code = "Life Insurance"
[ "What", "are", "the", "customer", "phone", "numbers", "under", "the", "policy", "\"", "Life", "Insurance", "\"", "?" ]
[ { "id": 0, "type": "table", "value": "available_policies" }, { "id": 2, "type": "column", "value": "policy_type_code" }, { "id": 1, "type": "column", "value": "customer_phone" }, { "id": 3, "type": "column", "value": "Life Insurance" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 3, 4 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 10, 11 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O" ]
14,909
law_episode
bird:train.json:1341
How many credits have been displayed from episode 1 until 10?
SELECT COUNT(T1.person_id) FROM Credit AS T1 INNER JOIN Episode AS T2 ON T1.episode_id = T2.episode_id WHERE T1.credited = 'true' AND T2.episode BETWEEN 1 AND 10
[ "How", "many", "credits", "have", "been", "displayed", "from", "episode", "1", "until", "10", "?" ]
[ { "id": 3, "type": "column", "value": "episode_id" }, { "id": 2, "type": "column", "value": "person_id" }, { "id": 4, "type": "column", "value": "credited" }, { "id": 1, "type": "table", "value": "episode" }, { "id": 6, "type": "column", "value": "episode" }, { "id": 0, "type": "table", "value": "credit" }, { "id": 5, "type": "value", "value": "true" }, { "id": 8, "type": "value", "value": "10" }, { "id": 7, "type": "value", "value": "1" } ]
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[ "O", "O", "B-TABLE", "O", "O", "O", "O", "B-COLUMN", "B-VALUE", "O", "B-VALUE", "O" ]
14,910
authors
bird:train.json:3640
How many journals don’t have a short name?
SELECT COUNT(ShortName) FROM Journal WHERE ShortName = ''
[ "How", "many", "journals", "do", "n’t", "have", "a", "short", "name", "?" ]
[ { "id": 1, "type": "column", "value": "shortname" }, { "id": 0, "type": "table", "value": "journal" } ]
[ { "entity_id": 0, "token_idxs": [ 2 ] }, { "entity_id": 1, "token_idxs": [ 7, 8 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-TABLE", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O" ]
14,911
dorm_1
spider:train_spider.json:5751
What are the names of the amenities that Smith Hall has?
SELECT T3.amenity_name FROM dorm AS T1 JOIN has_amenity AS T2 ON T1.dormid = T2.dormid JOIN dorm_amenity AS T3 ON T2.amenid = T3.amenid WHERE T1.dorm_name = 'Smith Hall'
[ "What", "are", "the", "names", "of", "the", "amenities", "that", "Smith", "Hall", "has", "?" ]
[ { "id": 0, "type": "column", "value": "amenity_name" }, { "id": 1, "type": "table", "value": "dorm_amenity" }, { "id": 5, "type": "table", "value": "has_amenity" }, { "id": 3, "type": "value", "value": "Smith Hall" }, { "id": 2, "type": "column", "value": "dorm_name" }, { "id": 6, "type": "column", "value": "amenid" }, { "id": 7, "type": "column", "value": "dormid" }, { "id": 4, "type": "table", "value": "dorm" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 8, 9 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [ 6 ] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "B-VALUE", "I-VALUE", "O", "O" ]
14,912
shakespeare
bird:train.json:3004
How many acts are there in Sonnets?
SELECT SUM(DISTINCT T2.Act) FROM works AS T1 INNER JOIN chapters AS T2 ON T1.id = T2.work_id WHERE T1.Title = 'Sonnets'
[ "How", "many", "acts", "are", "there", "in", "Sonnets", "?" ]
[ { "id": 1, "type": "table", "value": "chapters" }, { "id": 3, "type": "value", "value": "Sonnets" }, { "id": 6, "type": "column", "value": "work_id" }, { "id": 0, "type": "table", "value": "works" }, { "id": 2, "type": "column", "value": "title" }, { "id": 4, "type": "column", "value": "act" }, { "id": 5, "type": "column", "value": "id" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 6 ] }, { "entity_id": 4, "token_idxs": [ 2 ] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-COLUMN", "O", "O", "O", "B-VALUE", "O" ]
14,913
ice_hockey_draft
bird:train.json:6960
What is the percentage of players who were born in Denmark and weight above 154 lbs?
SELECT CAST(COUNT(CASE WHEN T1.nation = 'Denmark' AND T2.weight_in_lbs > 154 THEN T1.ELITEID ELSE NULL END) AS REAL) * 100 / COUNT(T1.ELITEID) FROM PlayerInfo AS T1 INNER JOIN weight_info AS T2 ON T1.weight = T2.weight_id
[ "What", "is", "the", "percentage", "of", "players", "who", "were", "born", "in", "Denmark", "and", "weight", "above", "154", "lbs", "?" ]
[ { "id": 8, "type": "column", "value": "weight_in_lbs" }, { "id": 1, "type": "table", "value": "weight_info" }, { "id": 0, "type": "table", "value": "playerinfo" }, { "id": 3, "type": "column", "value": "weight_id" }, { "id": 5, "type": "column", "value": "eliteid" }, { "id": 7, "type": "value", "value": "Denmark" }, { "id": 2, "type": "column", "value": "weight" }, { "id": 6, "type": "column", "value": "nation" }, { "id": 4, "type": "value", "value": "100" }, { "id": 9, "type": "value", "value": "154" } ]
[ { "entity_id": 0, "token_idxs": [ 5 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 12 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [ 10 ] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [ 14 ] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "O", "B-VALUE", "O", "B-COLUMN", "O", "B-VALUE", "O", "O" ]
14,914
public_review_platform
bird:train.json:4090
Sum up the likes get by short reviews on businesses located in City Goodyear.
SELECT SUM(T2.likes) AS likes FROM Business AS T1 INNER JOIN Tips AS T2 ON T1.business_id = T2.business_id WHERE T1.city = 'Goodyear'
[ "Sum", "up", "the", "likes", "get", "by", "short", "reviews", "on", "businesses", "located", "in", "City", "Goodyear", "." ]
[ { "id": 5, "type": "column", "value": "business_id" }, { "id": 0, "type": "table", "value": "business" }, { "id": 3, "type": "value", "value": "Goodyear" }, { "id": 4, "type": "column", "value": "likes" }, { "id": 1, "type": "table", "value": "tips" }, { "id": 2, "type": "column", "value": "city" } ]
[ { "entity_id": 0, "token_idxs": [ 9 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 12 ] }, { "entity_id": 3, "token_idxs": [ 13 ] }, { "entity_id": 4, "token_idxs": [ 3 ] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-COLUMN", "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "B-COLUMN", "B-VALUE", "O" ]
14,915
tracking_software_problems
spider:train_spider.json:5352
What is the id of the problem log that is created most recently?
SELECT problem_log_id FROM problem_log ORDER BY log_entry_date DESC LIMIT 1
[ "What", "is", "the", "i", "d", "of", "the", "problem", "log", "that", "is", "created", "most", "recently", "?" ]
[ { "id": 1, "type": "column", "value": "problem_log_id" }, { "id": 2, "type": "column", "value": "log_entry_date" }, { "id": 0, "type": "table", "value": "problem_log" } ]
[ { "entity_id": 0, "token_idxs": [ 7, 8 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "I-TABLE", "O", "O", "O", "O", "O", "O" ]
14,916
sing_contest
bird:test.json:744
What are the maximum and minimum voice sound quality score of the performances?
SELECT max(voice_sound_quality) , min(voice_sound_quality) FROM performance_score
[ "What", "are", "the", "maximum", "and", "minimum", "voice", "sound", "quality", "score", "of", "the", "performances", "?" ]
[ { "id": 1, "type": "column", "value": "voice_sound_quality" }, { "id": 0, "type": "table", "value": "performance_score" } ]
[ { "entity_id": 0, "token_idxs": [ 12 ] }, { "entity_id": 1, "token_idxs": [ 6, 7, 8 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "O", "O", "O", "B-TABLE", "O" ]
14,917
toxicology
bird:dev.json:222
What is the difference between the number of molecules that are carcinogenic and those that are not?
SELECT COUNT(CASE WHEN T.label = '+' THEN T.molecule_id ELSE NULL END) - COUNT(CASE WHEN T.label = '-' THEN T.molecule_id ELSE NULL END) AS diff_car_notcar FROM molecule t
[ "What", "is", "the", "difference", "between", "the", "number", "of", "molecules", "that", "are", "carcinogenic", "and", "those", "that", "are", "not", "?" ]
[ { "id": 1, "type": "column", "value": "molecule_id" }, { "id": 0, "type": "table", "value": "molecule" }, { "id": 2, "type": "column", "value": "label" }, { "id": 3, "type": "value", "value": "+" }, { "id": 4, "type": "value", "value": "-" } ]
[ { "entity_id": 0, "token_idxs": [ 8 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "O", "O", "O" ]
14,918
mondial_geo
bird:train.json:8363
Which Arabic-speaking country has the smallest population?
SELECT T1.Name FROM country AS T1 INNER JOIN language AS T2 ON T1.Code = T2.Country WHERE T2.Name = 'Arabic' AND T2.Percentage = 100 ORDER BY T1.Population ASC LIMIT 1
[ "Which", "Arabic", "-", "speaking", "country", "has", "the", "smallest", "population", "?" ]
[ { "id": 3, "type": "column", "value": "population" }, { "id": 7, "type": "column", "value": "percentage" }, { "id": 2, "type": "table", "value": "language" }, { "id": 1, "type": "table", "value": "country" }, { "id": 5, "type": "column", "value": "country" }, { "id": 6, "type": "value", "value": "Arabic" }, { "id": 0, "type": "column", "value": "name" }, { "id": 4, "type": "column", "value": "code" }, { "id": 8, "type": "value", "value": "100" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 8 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [ 4 ] }, { "entity_id": 6, "token_idxs": [ 1 ] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "B-VALUE", "O", "O", "B-COLUMN", "O", "O", "O", "B-COLUMN", "O" ]
14,919
bakery_1
bird:test.json:1489
What is the cheapest cookie and its flavor?
SELECT id , flavor FROM goods WHERE food = "Cookie" ORDER BY price LIMIT 1
[ "What", "is", "the", "cheapest", "cookie", "and", "its", "flavor", "?" ]
[ { "id": 2, "type": "column", "value": "flavor" }, { "id": 4, "type": "column", "value": "Cookie" }, { "id": 0, "type": "table", "value": "goods" }, { "id": 5, "type": "column", "value": "price" }, { "id": 3, "type": "column", "value": "food" }, { "id": 1, "type": "column", "value": "id" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 7 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [ 4 ] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "B-COLUMN", "O", "O", "B-COLUMN", "O" ]
14,920
college_2
spider:train_spider.json:1456
Find the name of students who have taken the prerequisite course of the course with title International Finance.
SELECT T1.name FROM student AS T1 JOIN takes AS T2 ON T1.id = T2.id WHERE T2.course_id IN (SELECT T4.prereq_id FROM course AS T3 JOIN prereq AS T4 ON T3.course_id = T4.course_id WHERE T3.title = 'International Finance')
[ "Find", "the", "name", "of", "students", "who", "have", "taken", "the", "prerequisite", "course", "of", "the", "course", "with", "title", "International", "Finance", "." ]
[ { "id": 9, "type": "value", "value": "International Finance" }, { "id": 3, "type": "column", "value": "course_id" }, { "id": 5, "type": "column", "value": "prereq_id" }, { "id": 1, "type": "table", "value": "student" }, { "id": 6, "type": "table", "value": "course" }, { "id": 7, "type": "table", "value": "prereq" }, { "id": 2, "type": "table", "value": "takes" }, { "id": 8, "type": "column", "value": "title" }, { "id": 0, "type": "column", "value": "name" }, { "id": 4, "type": "column", "value": "id" } ]
[ { "entity_id": 0, "token_idxs": [ 2 ] }, { "entity_id": 1, "token_idxs": [ 4 ] }, { "entity_id": 2, "token_idxs": [ 7 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [ 0 ] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [ 13 ] }, { "entity_id": 7, "token_idxs": [ 9 ] }, { "entity_id": 8, "token_idxs": [ 15 ] }, { "entity_id": 9, "token_idxs": [ 16, 17 ] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "B-COLUMN", "O", "B-COLUMN", "O", "B-TABLE", "O", "O", "B-TABLE", "O", "B-TABLE", "O", "O", "O", "B-TABLE", "O", "B-COLUMN", "B-VALUE", "I-VALUE", "O" ]
14,921
csu_1
spider:train_spider.json:2388
What is the campus fee of "San Francisco State University" in year 2000?
SELECT t1.campusfee FROM csu_fees AS t1 JOIN campuses AS t2 ON t1.campus = t2.id WHERE t2.campus = "San Francisco State University" AND t1.year = 2000
[ "What", "is", "the", "campus", "fee", "of", "\"", "San", "Francisco", "State", "University", "\"", "in", "year", "2000", "?" ]
[ { "id": 5, "type": "column", "value": "San Francisco State University" }, { "id": 0, "type": "column", "value": "campusfee" }, { "id": 1, "type": "table", "value": "csu_fees" }, { "id": 2, "type": "table", "value": "campuses" }, { "id": 3, "type": "column", "value": "campus" }, { "id": 6, "type": "column", "value": "year" }, { "id": 7, "type": "value", "value": "2000" }, { "id": 4, "type": "column", "value": "id" } ]
[ { "entity_id": 0, "token_idxs": [ 4 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 3 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [ 7, 8, 9, 10 ] }, { "entity_id": 6, "token_idxs": [ 13 ] }, { "entity_id": 7, "token_idxs": [ 14 ] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-COLUMN", "B-COLUMN", "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "I-COLUMN", "O", "O", "B-COLUMN", "B-VALUE", "O" ]
14,922
pilot_1
bird:test.json:1145
Find pilots who own planes Piper Cub and B-52 Bomber.
SELECT pilot_name FROM pilotskills WHERE plane_name = 'Piper Cub' INTERSECT SELECT pilot_name FROM pilotskills WHERE plane_name = 'B-52 Bomber'
[ "Find", "pilots", "who", "own", "planes", "Piper", "Cub", "and", "B-52", "Bomber", "." ]
[ { "id": 0, "type": "table", "value": "pilotskills" }, { "id": 4, "type": "value", "value": "B-52 Bomber" }, { "id": 1, "type": "column", "value": "pilot_name" }, { "id": 2, "type": "column", "value": "plane_name" }, { "id": 3, "type": "value", "value": "Piper Cub" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 1 ] }, { "entity_id": 2, "token_idxs": [ 4 ] }, { "entity_id": 3, "token_idxs": [ 5, 6 ] }, { "entity_id": 4, "token_idxs": [ 8, 9 ] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "B-COLUMN", "O", "O", "B-COLUMN", "B-VALUE", "I-VALUE", "O", "B-VALUE", "I-VALUE", "O" ]
14,923
e_commerce
bird:test.json:61
How many orders has each gender placed?
SELECT T1.gender_code , count(*) FROM Customers AS T1 JOIN Orders AS T2 ON T1.customer_id = T2.customer_id GROUP BY T1.gender_code
[ "How", "many", "orders", "has", "each", "gender", "placed", "?" ]
[ { "id": 0, "type": "column", "value": "gender_code" }, { "id": 3, "type": "column", "value": "customer_id" }, { "id": 1, "type": "table", "value": "customers" }, { "id": 2, "type": "table", "value": "orders" } ]
[ { "entity_id": 0, "token_idxs": [ 5 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 2 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-TABLE", "O", "O", "B-COLUMN", "O", "O" ]
14,924
movie_3
bird:train.json:9130
Among the films that the customer RUTH MARTINEZ has rented, how many of them are released in 2006?
SELECT COUNT(T1.customer_id) FROM customer AS T1 INNER JOIN rental AS T2 ON T1.customer_id = T2.customer_id INNER JOIN inventory AS T3 ON T2.inventory_id = T3.inventory_id INNER JOIN film AS T4 ON T3.film_id = T4.film_id WHERE T4.release_year = 2006 AND T1.first_name = 'RUTH' AND T1.last_name = 'MARTINEZ'
[ "Among", "the", "films", "that", "the", "customer", "RUTH", "MARTINEZ", "has", "rented", ",", "how", "many", "of", "them", "are", "released", "in", "2006", "?" ]
[ { "id": 4, "type": "column", "value": "release_year" }, { "id": 12, "type": "column", "value": "inventory_id" }, { "id": 1, "type": "column", "value": "customer_id" }, { "id": 6, "type": "column", "value": "first_name" }, { "id": 2, "type": "table", "value": "inventory" }, { "id": 8, "type": "column", "value": "last_name" }, { "id": 9, "type": "value", "value": "MARTINEZ" }, { "id": 10, "type": "table", "value": "customer" }, { "id": 3, "type": "column", "value": "film_id" }, { "id": 11, "type": "table", "value": "rental" }, { "id": 0, "type": "table", "value": "film" }, { "id": 5, "type": "value", "value": "2006" }, { "id": 7, "type": "value", "value": "RUTH" } ]
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[ "O", "O", "B-TABLE", "O", "O", "B-TABLE", "B-VALUE", "B-VALUE", "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "B-VALUE", "O" ]
14,925
movie_platform
bird:train.json:60
Please list the titles of the movie lists user 32172230 created when he or she was eligible for trial.
SELECT T1.list_title FROM lists AS T1 INNER JOIN lists_users AS T2 ON T1.list_id = T2.list_id AND T1.user_id = T2.user_id WHERE T1.user_id = 32172230 AND T2.user_eligible_for_trial = 1
[ "Please", "list", "the", "titles", "of", "the", "movie", "lists", "user", "32172230", "created", "when", "he", "or", "she", "was", "eligible", "for", "trial", "." ]
[ { "id": 5, "type": "column", "value": "user_eligible_for_trial" }, { "id": 2, "type": "table", "value": "lists_users" }, { "id": 0, "type": "column", "value": "list_title" }, { "id": 4, "type": "value", "value": "32172230" }, { "id": 3, "type": "column", "value": "user_id" }, { "id": 7, "type": "column", "value": "list_id" }, { "id": 1, "type": "table", "value": "lists" }, { "id": 6, "type": "value", "value": "1" } ]
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[ "O", "B-COLUMN", "B-COLUMN", "I-COLUMN", "O", "O", "O", "B-TABLE", "B-COLUMN", "B-VALUE", "O", "O", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "O" ]
14,926
talkingdata
bird:train.json:1240
List all females aged 24 to 26 devices' locations.
SELECT T2.longitude, T2.latitude FROM gender_age AS T1 INNER JOIN events_relevant AS T2 ON T1.device_id = T2.device_id WHERE T1.`group` = 'F24-26' AND T1.gender = 'F'
[ "List", "all", "females", "aged", "24", "to", "26", "devices", "'", "locations", "." ]
[ { "id": 3, "type": "table", "value": "events_relevant" }, { "id": 2, "type": "table", "value": "gender_age" }, { "id": 0, "type": "column", "value": "longitude" }, { "id": 4, "type": "column", "value": "device_id" }, { "id": 1, "type": "column", "value": "latitude" }, { "id": 6, "type": "value", "value": "F24-26" }, { "id": 7, "type": "column", "value": "gender" }, { "id": 5, "type": "column", "value": "group" }, { "id": 8, "type": "value", "value": "F" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [ 7 ] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [ 3 ] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-COLUMN", "O", "O", "O", "B-COLUMN", "O", "O", "O" ]
14,927
address
bird:train.json:5136
Calculate the ratio between the number of representatives in Alabama and the number of representatives in Illinois.
SELECT CAST(COUNT(CASE WHEN state = 'Alabama' THEN cognress_rep_id ELSE NULL END) AS REAL) / COUNT(CASE WHEN state = 'Illinois' THEN cognress_rep_id ELSE NULL END) FROM congress
[ "Calculate", "the", "ratio", "between", "the", "number", "of", "representatives", "in", "Alabama", "and", "the", "number", "of", "representatives", "in", "Illinois", "." ]
[ { "id": 1, "type": "column", "value": "cognress_rep_id" }, { "id": 0, "type": "table", "value": "congress" }, { "id": 3, "type": "value", "value": "Illinois" }, { "id": 4, "type": "value", "value": "Alabama" }, { "id": 2, "type": "column", "value": "state" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 16 ] }, { "entity_id": 4, "token_idxs": [ 9 ] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-VALUE", "O", "O", "O", "O", "O", "O", "B-VALUE", "O" ]
14,928
movielens
bird:train.json:2343
What is the percentage difference of English and non-English-language crime movies in other countries in year 3?
SELECT CAST(SUM(IIF(T1.isEnglish = 'T', 1, 0)) - SUM(IIF(T1.isEnglish = 'F', 1, 0)) AS REAL) * 100 / COUNT(T1.movieid) FROM movies AS T1 INNER JOIN movies2directors AS T2 ON T1.movieid = T2.movieid WHERE T1.country = 'other' AND T1.year = 3
[ "What", "is", "the", "percentage", "difference", "of", "English", "and", "non", "-", "English", "-", "language", "crime", "movies", "in", "other", "countries", "in", "year", "3", "?" ]
[ { "id": 1, "type": "table", "value": "movies2directors" }, { "id": 10, "type": "column", "value": "isenglish" }, { "id": 2, "type": "column", "value": "movieid" }, { "id": 3, "type": "column", "value": "country" }, { "id": 0, "type": "table", "value": "movies" }, { "id": 4, "type": "value", "value": "other" }, { "id": 5, "type": "column", "value": "year" }, { "id": 7, "type": "value", "value": "100" }, { "id": 6, "type": "value", "value": "3" }, { "id": 8, "type": "value", "value": "1" }, { "id": 9, "type": "value", "value": "0" }, { "id": 11, "type": "value", "value": "T" }, { "id": 12, "type": "value", "value": "F" } ]
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[ "O", "O", "O", "O", "O", "B-VALUE", "O", "O", "O", "O", "B-COLUMN", "O", "O", "O", "B-TABLE", "B-TABLE", "B-VALUE", "B-COLUMN", "O", "B-COLUMN", "B-VALUE", "O" ]
14,929
student_1
spider:train_spider.json:4086
Report the number of students in each classroom.
SELECT classroom , count(*) FROM list GROUP BY classroom
[ "Report", "the", "number", "of", "students", "in", "each", "classroom", "." ]
[ { "id": 1, "type": "column", "value": "classroom" }, { "id": 0, "type": "table", "value": "list" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 7 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O" ]
14,930
world_development_indicators
bird:train.json:2120
Which countries have a fertility rate between 4 and 5 in 1979? List their names.
SELECT CountryName FROM Indicators WHERE Year = 1979 AND IndicatorName = 'Fertility rate, total (births per woman)' AND value >= 4 AND Value <= 5
[ "Which", "countries", "have", "a", "fertility", "rate", "between", "4", "and", "5", "in", "1979", "?", "List", "their", "names", "." ]
[ { "id": 5, "type": "value", "value": "Fertility rate, total (births per woman)" }, { "id": 4, "type": "column", "value": "indicatorname" }, { "id": 1, "type": "column", "value": "countryname" }, { "id": 0, "type": "table", "value": "indicators" }, { "id": 6, "type": "column", "value": "value" }, { "id": 2, "type": "column", "value": "year" }, { "id": 3, "type": "value", "value": "1979" }, { "id": 7, "type": "value", "value": "4" }, { "id": 8, "type": "value", "value": "5" } ]
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[ "O", "B-COLUMN", "O", "O", "B-VALUE", "I-VALUE", "I-VALUE", "B-VALUE", "B-VALUE", "B-VALUE", "O", "B-VALUE", "O", "O", "O", "O", "O" ]
14,931
synthea
bird:train.json:1524
List all the full names of patients with a condition described as cystitis.
SELECT DISTINCT T1.first, T1.last FROM patients AS T1 INNER JOIN conditions AS T2 ON T1.patient = T2.patient WHERE T2.DESCRIPTION = 'Cystitis'
[ "List", "all", "the", "full", "names", "of", "patients", "with", "a", "condition", "described", "as", "cystitis", "." ]
[ { "id": 4, "type": "column", "value": "description" }, { "id": 3, "type": "table", "value": "conditions" }, { "id": 2, "type": "table", "value": "patients" }, { "id": 5, "type": "value", "value": "Cystitis" }, { "id": 6, "type": "column", "value": "patient" }, { "id": 0, "type": "column", "value": "first" }, { "id": 1, "type": "column", "value": "last" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 0 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 9 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [ 12 ] }, { "entity_id": 6, "token_idxs": [ 6 ] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "B-COLUMN", "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "B-VALUE", "O" ]
14,932
hockey
bird:train.json:7617
Name the goalies who played for more than two teams from Year 2000 to 2005.
SELECT T1.firstName, T1.lastName FROM Master AS T1 INNER JOIN Goalies AS T2 ON T1.playerID = T2.playerID WHERE T2.year >= 2000 AND T2.year <= 2005 GROUP BY T2.playerID HAVING COUNT(DISTINCT T2.tmID) > 2
[ "Name", "the", "goalies", "who", "played", "for", "more", "than", "two", "teams", "from", "Year", "2000", "to", "2005", "." ]
[ { "id": 1, "type": "column", "value": "firstname" }, { "id": 0, "type": "column", "value": "playerid" }, { "id": 2, "type": "column", "value": "lastname" }, { "id": 4, "type": "table", "value": "goalies" }, { "id": 3, "type": "table", "value": "master" }, { "id": 6, "type": "column", "value": "year" }, { "id": 7, "type": "value", "value": "2000" }, { "id": 8, "type": "value", "value": "2005" }, { "id": 9, "type": "column", "value": "tmid" }, { "id": 5, "type": "value", "value": "2" } ]
[ { "entity_id": 0, "token_idxs": [ 4 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 0 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [ 2 ] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [ 11 ] }, { "entity_id": 7, "token_idxs": [ 12 ] }, { "entity_id": 8, "token_idxs": [ 14 ] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "B-COLUMN", "O", "B-TABLE", "O", "B-COLUMN", "O", "O", "O", "O", "O", "O", "B-COLUMN", "B-VALUE", "O", "B-VALUE", "O" ]
14,933
movie_platform
bird:train.json:153
What is the URL to the movie director page on Mubi of the movie titled "Red Blooded American Girl"
SELECT director_url FROM movies WHERE movie_title LIKE 'Red Blooded American Girl'
[ "What", "is", "the", "URL", "to", "the", "movie", "director", "page", "on", "Mubi", "of", "the", "movie", "titled", "\"", "Red", "Blooded", "American", "Girl", "\"" ]
[ { "id": 3, "type": "value", "value": "Red Blooded American Girl" }, { "id": 1, "type": "column", "value": "director_url" }, { "id": 2, "type": "column", "value": "movie_title" }, { "id": 0, "type": "table", "value": "movies" } ]
[ { "entity_id": 0, "token_idxs": [ 13 ] }, { "entity_id": 1, "token_idxs": [ 7 ] }, { "entity_id": 2, "token_idxs": [ 14 ] }, { "entity_id": 3, "token_idxs": [ 16, 17, 18, 19 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "O", "O", "O", "B-TABLE", "B-COLUMN", "O", "B-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "O" ]
14,934
music_1
spider:train_spider.json:3574
What are the names of the artists who released a song that has the word love in its title, and where are the artists from?
SELECT T1.artist_name , T1.country FROM artist AS T1 JOIN song AS T2 ON T1.artist_name = T2.artist_name WHERE T2.song_name LIKE "%love%"
[ "What", "are", "the", "names", "of", "the", "artists", "who", "released", "a", "song", "that", "has", "the", "word", "love", "in", "its", "title", ",", "and", "where", "are", "the", "artists", "from", "?" ]
[ { "id": 0, "type": "column", "value": "artist_name" }, { "id": 4, "type": "column", "value": "song_name" }, { "id": 1, "type": "column", "value": "country" }, { "id": 2, "type": "table", "value": "artist" }, { "id": 5, "type": "column", "value": "%love%" }, { "id": 3, "type": "table", "value": "song" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "O", "B-COLUMN", "O", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "O" ]
14,935
movies_4
bird:train.json:481
List 10 movie titles that were produced in France.
SELECT T1.title FROM movie AS T1 INNER JOIN production_COUNTry AS T2 ON T1.movie_id = T2.movie_id INNER JOIN COUNTry AS T3 ON T2.COUNTry_id = T3.COUNTry_id WHERE T3.COUNTry_name = 'France' LIMIT 10
[ "List", "10", "movie", "titles", "that", "were", "produced", "in", "France", "." ]
[ { "id": 5, "type": "table", "value": "production_country" }, { "id": 2, "type": "column", "value": "country_name" }, { "id": 6, "type": "column", "value": "country_id" }, { "id": 7, "type": "column", "value": "movie_id" }, { "id": 1, "type": "table", "value": "country" }, { "id": 3, "type": "value", "value": "France" }, { "id": 0, "type": "column", "value": "title" }, { "id": 4, "type": "table", "value": "movie" } ]
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[ "O", "O", "B-TABLE", "B-COLUMN", "O", "O", "O", "O", "B-VALUE", "O" ]
14,936
food_inspection_2
bird:train.json:6122
Among the facilities that had undergone at least one inspection in 2010, how many of them have the most serious food safety issues?
SELECT COUNT(DISTINCT T2.license_no) FROM inspection AS T1 INNER JOIN establishment AS T2 ON T1.license_no = T2.license_no WHERE strftime('%Y', T1.inspection_date) = '2010' AND T2.risk_level = 3
[ "Among", "the", "facilities", "that", "had", "undergone", "at", "least", "one", "inspection", "in", "2010", ",", "how", "many", "of", "them", "have", "the", "most", "serious", "food", "safety", "issues", "?" ]
[ { "id": 7, "type": "column", "value": "inspection_date" }, { "id": 1, "type": "table", "value": "establishment" }, { "id": 0, "type": "table", "value": "inspection" }, { "id": 2, "type": "column", "value": "license_no" }, { "id": 4, "type": "column", "value": "risk_level" }, { "id": 3, "type": "value", "value": "2010" }, { "id": 6, "type": "value", "value": "%Y" }, { "id": 5, "type": "value", "value": "3" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "B-VALUE", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O" ]
14,937
epinions_1
spider:train_spider.json:1690
List all information in the item table.
SELECT * FROM item
[ "List", "all", "information", "in", "the", "item", "table", "." ]
[ { "id": 0, "type": "table", "value": "item" } ]
[ { "entity_id": 0, "token_idxs": [ 5 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "B-TABLE", "O", "O" ]
14,938
body_builder
spider:train_spider.json:1150
How many body builders are there?
SELECT count(*) FROM body_builder
[ "How", "many", "body", "builders", "are", "there", "?" ]
[ { "id": 0, "type": "table", "value": "body_builder" } ]
[ { "entity_id": 0, "token_idxs": [ 2, 3 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-TABLE", "I-TABLE", "O", "O", "O" ]
14,939
school_player
spider:train_spider.json:4888
Please show different denominations and the corresponding number of schools in descending order.
SELECT Denomination , COUNT(*) FROM school GROUP BY Denomination ORDER BY COUNT(*) DESC
[ "Please", "show", "different", "denominations", "and", "the", "corresponding", "number", "of", "schools", "in", "descending", "order", "." ]
[ { "id": 1, "type": "column", "value": "denomination" }, { "id": 0, "type": "table", "value": "school" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "O" ]
14,940
restaurant
bird:train.json:1762
In restaurants with a review of 2, how many restaurants have a street number below 500?
SELECT COUNT(T1.id_restaurant) FROM location AS T1 INNER JOIN generalinfo AS T2 ON T1.id_restaurant = T2.id_restaurant WHERE T2.review = 2 AND T1.street_num < 500
[ "In", "restaurants", "with", "a", "review", "of", "2", ",", "how", "many", "restaurants", "have", "a", "street", "number", "below", "500", "?" ]
[ { "id": 2, "type": "column", "value": "id_restaurant" }, { "id": 1, "type": "table", "value": "generalinfo" }, { "id": 5, "type": "column", "value": "street_num" }, { "id": 0, "type": "table", "value": "location" }, { "id": 3, "type": "column", "value": "review" }, { "id": 6, "type": "value", "value": "500" }, { "id": 4, "type": "value", "value": "2" } ]
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[ "O", "O", "O", "O", "B-COLUMN", "O", "B-VALUE", "O", "O", "O", "B-COLUMN", "O", "O", "B-COLUMN", "I-COLUMN", "O", "B-VALUE", "O" ]
14,941
school_player
spider:train_spider.json:4864
List the locations of schools in descending order of founded year.
SELECT LOCATION FROM school ORDER BY Founded DESC
[ "List", "the", "locations", "of", "schools", "in", "descending", "order", "of", "founded", "year", "." ]
[ { "id": 1, "type": "column", "value": "location" }, { "id": 2, "type": "column", "value": "founded" }, { "id": 0, "type": "table", "value": "school" } ]
[ { "entity_id": 0, "token_idxs": [ 4 ] }, { "entity_id": 1, "token_idxs": [ 2 ] }, { "entity_id": 2, "token_idxs": [ 9 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-COLUMN", "O", "B-TABLE", "O", "O", "O", "O", "B-COLUMN", "O", "O" ]
14,942
company_office
spider:train_spider.json:4545
List the names of companies in descending order of market value.
SELECT name FROM Companies ORDER BY Market_Value_billion DESC
[ "List", "the", "names", "of", "companies", "in", "descending", "order", "of", "market", "value", "." ]
[ { "id": 2, "type": "column", "value": "market_value_billion" }, { "id": 0, "type": "table", "value": "companies" }, { "id": 1, "type": "column", "value": "name" } ]
[ { "entity_id": 0, "token_idxs": [ 4 ] }, { "entity_id": 1, "token_idxs": [ 2 ] }, { "entity_id": 2, "token_idxs": [ 9, 10 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-COLUMN", "O", "B-TABLE", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O" ]
14,943
human_resources
bird:train.json:8942
Among the employees who are Trainees, how many of them work in New York?
SELECT COUNT(*) FROM employee AS T1 INNER JOIN location AS T2 ON T1.locationID = T2.locationID INNER JOIN position AS T3 ON T3.positionID = T1.positionID WHERE T3.positiontitle = 'Trainee' AND T2.state = 'NY'
[ "Among", "the", "employees", "who", "are", "Trainees", ",", "how", "many", "of", "them", "work", "in", "New", "York", "?" ]
[ { "id": 4, "type": "column", "value": "positiontitle" }, { "id": 3, "type": "column", "value": "positionid" }, { "id": 8, "type": "column", "value": "locationid" }, { "id": 0, "type": "table", "value": "position" }, { "id": 1, "type": "table", "value": "employee" }, { "id": 2, "type": "table", "value": "location" }, { "id": 5, "type": "value", "value": "Trainee" }, { "id": 6, "type": "column", "value": "state" }, { "id": 7, "type": "value", "value": "NY" } ]
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[ "O", "O", "B-TABLE", "O", "O", "B-VALUE", "O", "O", "B-VALUE", "O", "O", "O", "O", "O", "O", "O" ]
14,944
hockey
bird:train.json:7715
Who was the most clutch player in 1986? Give his full name.
SELECT T1.firstName, T1.lastName FROM Master AS T1 INNER JOIN Scoring AS T2 ON T1.playerID = T2.playerID WHERE T2.year = 1986 GROUP BY T2.playerID ORDER BY SUM(T2.GWG) DESC LIMIT 1
[ "Who", "was", "the", "most", "clutch", "player", "in", "1986", "?", "Give", "his", "full", "name", "." ]
[ { "id": 1, "type": "column", "value": "firstname" }, { "id": 0, "type": "column", "value": "playerid" }, { "id": 2, "type": "column", "value": "lastname" }, { "id": 4, "type": "table", "value": "scoring" }, { "id": 3, "type": "table", "value": "master" }, { "id": 5, "type": "column", "value": "year" }, { "id": 6, "type": "value", "value": "1986" }, { "id": 7, "type": "column", "value": "gwg" } ]
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[ "O", "B-TABLE", "I-TABLE", "O", "O", "B-COLUMN", "O", "B-VALUE", "O", "O", "O", "O", "B-COLUMN", "O" ]
14,945
legislator
bird:train.json:4853
Which legislators do not have instagram account?
SELECT T1.first_name, T1.last_name FROM current AS T1 INNER JOIN `social-media` AS T2 ON T1.bioguide_id = T2.bioguide WHERE T2.instagram IS NULL
[ "Which", "legislators", "do", "not", "have", "instagram", "account", "?" ]
[ { "id": 3, "type": "table", "value": "social-media" }, { "id": 5, "type": "column", "value": "bioguide_id" }, { "id": 0, "type": "column", "value": "first_name" }, { "id": 1, "type": "column", "value": "last_name" }, { "id": 4, "type": "column", "value": "instagram" }, { "id": 6, "type": "column", "value": "bioguide" }, { "id": 2, "type": "table", "value": "current" } ]
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[ "O", "O", "O", "O", "O", "B-COLUMN", "O", "O" ]
14,946
regional_sales
bird:train.json:2711
Please indicate store id in the state of California that have been applied 20% discount in store.
SELECT T FROM ( SELECT DISTINCT CASE WHEN T2.State = 'California' AND T1.`Sales Channel` = 'In-Store' AND T1.`Discount Applied` = 0.2 THEN T2.StoreID END AS T FROM `Sales Orders` T1 INNER JOIN `Store Locations` T2 ON T2.StoreID = T1._StoreID ) WHERE T IS NOT NULL
[ "Please", "indicate", "store", "i", "d", "in", "the", "state", "of", "California", "that", "have", "been", "applied", "20", "%", "discount", "in", "store", "." ]
[ { "id": 9, "type": "column", "value": "Discount Applied" }, { "id": 2, "type": "table", "value": "Store Locations" }, { "id": 7, "type": "column", "value": "Sales Channel" }, { "id": 1, "type": "table", "value": "Sales Orders" }, { "id": 6, "type": "value", "value": "California" }, { "id": 4, "type": "column", "value": "_storeid" }, { "id": 8, "type": "value", "value": "In-Store" }, { "id": 3, "type": "column", "value": "storeid" }, { "id": 5, "type": "column", "value": "state" }, { "id": 10, "type": "value", "value": "0.2" }, { "id": 0, "type": "column", "value": "t" } ]
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[ "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "O", "O", "B-COLUMN", "O", "B-VALUE", "O", "O", "O", "O", "O", "O", "B-COLUMN", "B-VALUE", "I-VALUE", "O" ]
14,947
activity_1
spider:train_spider.json:6755
Show the faculty id of each faculty member, along with the number of students he or she advises.
SELECT T1.FacID , count(*) FROM Faculty AS T1 JOIN Student AS T2 ON T1.FacID = T2.advisor GROUP BY T1.FacID
[ "Show", "the", "faculty", "i", "d", "of", "each", "faculty", "member", ",", "along", "with", "the", "number", "of", "students", "he", "or", "she", "advises", "." ]
[ { "id": 1, "type": "table", "value": "faculty" }, { "id": 2, "type": "table", "value": "student" }, { "id": 3, "type": "column", "value": "advisor" }, { "id": 0, "type": "column", "value": "facid" } ]
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[ "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "B-COLUMN", "O" ]
14,948
student_assessment
spider:train_spider.json:84
What are the ids of the candidates that have an outcome code of Pass?
SELECT candidate_id FROM candidate_assessments WHERE asessment_outcome_code = "Pass"
[ "What", "are", "the", "ids", "of", "the", "candidates", "that", "have", "an", "outcome", "code", "of", "Pass", "?" ]
[ { "id": 2, "type": "column", "value": "asessment_outcome_code" }, { "id": 0, "type": "table", "value": "candidate_assessments" }, { "id": 1, "type": "column", "value": "candidate_id" }, { "id": 3, "type": "column", "value": "Pass" } ]
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[ "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "O", "B-COLUMN", "O" ]
14,949
retails
bird:train.json:6819
Which customer is the most in debt?
SELECT c_name FROM customer WHERE c_acctbal = ( SELECT MIN(c_acctbal) FROM customer )
[ "Which", "customer", "is", "the", "most", "in", "debt", "?" ]
[ { "id": 2, "type": "column", "value": "c_acctbal" }, { "id": 0, "type": "table", "value": "customer" }, { "id": 1, "type": "column", "value": "c_name" } ]
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[ "O", "B-TABLE", "O", "O", "O", "O", "O", "O" ]
14,950
cre_Doc_Control_Systems
spider:train_spider.json:2119
What address was the document with id 4 mailed to?
SELECT Addresses.address_details FROM Addresses JOIN Documents_Mailed ON Documents_Mailed.mailed_to_address_id = Addresses.address_id WHERE document_id = 4;
[ "What", "address", "was", "the", "document", "with", "i", "d", "4", "mailed", "to", "?" ]
[ { "id": 5, "type": "column", "value": "mailed_to_address_id" }, { "id": 2, "type": "table", "value": "documents_mailed" }, { "id": 0, "type": "column", "value": "address_details" }, { "id": 3, "type": "column", "value": "document_id" }, { "id": 6, "type": "column", "value": "address_id" }, { "id": 1, "type": "table", "value": "addresses" }, { "id": 4, "type": "value", "value": "4" } ]
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[ "O", "B-TABLE", "B-COLUMN", "O", "B-COLUMN", "O", "O", "O", "B-VALUE", "O", "O", "O" ]
14,951
store_1
spider:train_spider.json:593
Find the full name of employee who supported the most number of customers.
SELECT T1.first_name , T1.last_name FROM employees AS T1 JOIN customers AS T2 ON T1.id = T2.support_rep_id GROUP BY T1.id ORDER BY count(*) DESC LIMIT 1
[ "Find", "the", "full", "name", "of", "employee", "who", "supported", "the", "most", "number", "of", "customers", "." ]
[ { "id": 5, "type": "column", "value": "support_rep_id" }, { "id": 1, "type": "column", "value": "first_name" }, { "id": 2, "type": "column", "value": "last_name" }, { "id": 3, "type": "table", "value": "employees" }, { "id": 4, "type": "table", "value": "customers" }, { "id": 0, "type": "column", "value": "id" } ]
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[ "B-COLUMN", "O", "O", "B-COLUMN", "O", "B-TABLE", "O", "B-COLUMN", "O", "O", "O", "O", "B-TABLE", "O" ]
14,953
books
bird:train.json:5941
Calculate the percentage of the International shipping orders on 2022/11/10.
SELECT CAST(SUM(CASE WHEN T1.method_name = 'International' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(*) FROM shipping_method AS T1 INNER JOIN cust_order AS T2 ON T1.method_id = T2.shipping_method_id WHERE T2.order_date LIKE '2022-11-10%'
[ "Calculate", "the", "percentage", "of", "the", "International", "shipping", "orders", "on", "2022/11/10", "." ]
[ { "id": 5, "type": "column", "value": "shipping_method_id" }, { "id": 0, "type": "table", "value": "shipping_method" }, { "id": 10, "type": "value", "value": "International" }, { "id": 3, "type": "value", "value": "2022-11-10%" }, { "id": 9, "type": "column", "value": "method_name" }, { "id": 1, "type": "table", "value": "cust_order" }, { "id": 2, "type": "column", "value": "order_date" }, { "id": 4, "type": "column", "value": "method_id" }, { "id": 6, "type": "value", "value": "100" }, { "id": 7, "type": "value", "value": "0" }, { "id": 8, "type": "value", "value": "1" } ]
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[ "O", "O", "O", "O", "O", "B-VALUE", "B-TABLE", "B-COLUMN", "O", "B-VALUE", "O" ]
14,954
cars
bird:train.json:3067
What was the origin country of the car model ford torino produced in 1970?
SELECT T3.country FROM data AS T1 INNER JOIN production AS T2 ON T1.ID = T2.ID INNER JOIN country AS T3 ON T3.origin = T2.country WHERE T1.car_name = 'ford torino' AND T2.model_year = 1970
[ "What", "was", "the", "origin", "country", "of", "the", "car", "model", "ford", "torino", "produced", "in", "1970", "?" ]
[ { "id": 6, "type": "value", "value": "ford torino" }, { "id": 3, "type": "table", "value": "production" }, { "id": 7, "type": "column", "value": "model_year" }, { "id": 5, "type": "column", "value": "car_name" }, { "id": 0, "type": "column", "value": "country" }, { "id": 1, "type": "table", "value": "country" }, { "id": 4, "type": "column", "value": "origin" }, { "id": 2, "type": "table", "value": "data" }, { "id": 8, "type": "value", "value": "1970" }, { "id": 9, "type": "column", "value": "id" } ]
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[ "O", "O", "O", "B-COLUMN", "B-TABLE", "O", "O", "B-COLUMN", "I-COLUMN", "B-VALUE", "I-VALUE", "B-TABLE", "I-TABLE", "B-VALUE", "O" ]
14,955
authors
bird:train.json:3629
Tell the number of papers that were presented at "International Symposium on Software Testing and Analysis" conference.
SELECT COUNT(T1.Id) FROM Paper AS T1 INNER JOIN Conference AS T2 ON T1.ConferenceId = T2.Id WHERE T2.FullName = 'International Symposium on Software Testing and Analysis'
[ "Tell", "the", "number", "of", "papers", "that", "were", "presented", "at", "\"", "International", "Symposium", "on", "Software", "Testing", "and", "Analysis", "\"", "conference", "." ]
[ { "id": 3, "type": "value", "value": "International Symposium on Software Testing and Analysis" }, { "id": 5, "type": "column", "value": "conferenceid" }, { "id": 1, "type": "table", "value": "conference" }, { "id": 2, "type": "column", "value": "fullname" }, { "id": 0, "type": "table", "value": "paper" }, { "id": 4, "type": "column", "value": "id" } ]
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[ "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "B-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "O", "B-TABLE", "O" ]
14,956
shakespeare
bird:train.json:3028
How many "all" character names have the "all" abbreviation?
SELECT COUNT(id) FROM characters WHERE Abbrev = 'All'
[ "How", "many", "\"", "all", "\"", "character", "names", "have", "the", "\"", "all", "\"", "abbreviation", "?" ]
[ { "id": 0, "type": "table", "value": "characters" }, { "id": 1, "type": "column", "value": "abbrev" }, { "id": 2, "type": "value", "value": "All" }, { "id": 3, "type": "column", "value": "id" } ]
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[ "O", "O", "O", "B-VALUE", "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O" ]
14,957
shakespeare
bird:train.json:2969
When did Shakespeare write the first poem?
SELECT MIN(Date) FROM works WHERE GenreType = 'Poem'
[ "When", "did", "Shakespeare", "write", "the", "first", "poem", "?" ]
[ { "id": 1, "type": "column", "value": "genretype" }, { "id": 0, "type": "table", "value": "works" }, { "id": 2, "type": "value", "value": "Poem" }, { "id": 3, "type": "column", "value": "date" } ]
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[ "O", "O", "O", "O", "O", "O", "B-VALUE", "O" ]
14,958
customers_card_transactions
spider:train_spider.json:702
What are the valid from and valid to dates for the card with the number 4560596484842?
SELECT date_valid_from , date_valid_to FROM Customers_cards WHERE card_number = "4560596484842"
[ "What", "are", "the", "valid", "from", "and", "valid", "to", "dates", "for", "the", "card", "with", "the", "number", "4560596484842", "?" ]
[ { "id": 0, "type": "table", "value": "customers_cards" }, { "id": 1, "type": "column", "value": "date_valid_from" }, { "id": 2, "type": "column", "value": "date_valid_to" }, { "id": 4, "type": "column", "value": "4560596484842" }, { "id": 3, "type": "column", "value": "card_number" } ]
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[ "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "O", "B-COLUMN", "I-COLUMN", "O", "O", "O", "O", "O", "O", "B-COLUMN", "B-COLUMN", "O" ]
14,959
chicago_crime
bird:train.json:8646
How many crimes were handled by Brendan Reilly on 7th October 2018?
SELECT SUM(CASE WHEN T2.alderman_last_name = 'Reilly' THEN 1 ELSE 0 END) FROM Crime AS T1 INNER JOIN Ward AS T2 ON T1.ward_no = T2.ward_no WHERE T2.alderman_name_suffix IS NULL AND T2.alderman_first_name = 'Brendan' AND date LIKE '10/7/2018%'
[ "How", "many", "crimes", "were", "handled", "by", "Brendan", "Reilly", "on", "7th", "October", "2018", "?" ]
[ { "id": 3, "type": "column", "value": "alderman_name_suffix" }, { "id": 4, "type": "column", "value": "alderman_first_name" }, { "id": 10, "type": "column", "value": "alderman_last_name" }, { "id": 7, "type": "value", "value": "10/7/2018%" }, { "id": 2, "type": "column", "value": "ward_no" }, { "id": 5, "type": "value", "value": "Brendan" }, { "id": 11, "type": "value", "value": "Reilly" }, { "id": 0, "type": "table", "value": "crime" }, { "id": 1, "type": "table", "value": "ward" }, { "id": 6, "type": "column", "value": "date" }, { "id": 8, "type": "value", "value": "0" }, { "id": 9, "type": "value", "value": "1" } ]
[ { "entity_id": 0, "token_idxs": [ 2 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [ 6 ] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [ 7 ] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-TABLE", "O", "O", "O", "B-VALUE", "B-VALUE", "O", "O", "O", "O", "O" ]
14,961
art_1
bird:test.json:1269
What are the titles of all paintings and sculpture works made by the artist whose id is 222?
SELECT T2.title FROM artists AS T1 JOIN paintings AS T2 ON T1.artistID = T2.painterID WHERE T1.artistID = 222 UNION SELECT T4.title FROM artists AS T3 JOIN sculptures AS T4 ON T3.artistID = T4.sculptorID WHERE T3.artistID = 222
[ "What", "are", "the", "titles", "of", "all", "paintings", "and", "sculpture", "works", "made", "by", "the", "artist", "whose", "i", "d", "is", "222", "?" ]
[ { "id": 5, "type": "table", "value": "sculptures" }, { "id": 7, "type": "column", "value": "sculptorid" }, { "id": 2, "type": "table", "value": "paintings" }, { "id": 6, "type": "column", "value": "painterid" }, { "id": 3, "type": "column", "value": "artistid" }, { "id": 1, "type": "table", "value": "artists" }, { "id": 0, "type": "column", "value": "title" }, { "id": 4, "type": "value", "value": "222" } ]
[ { "entity_id": 0, "token_idxs": [ 3 ] }, { "entity_id": 1, "token_idxs": [ 13 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [ 18 ] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [ 6 ] }, { "entity_id": 7, "token_idxs": [ 8 ] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-COLUMN", "O", "O", "B-COLUMN", "O", "B-COLUMN", "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "O", "B-VALUE", "O" ]
14,962
simpson_episodes
bird:train.json:4263
How much more votes for episode 1 than for episode 5?
SELECT SUM(CASE WHEN T1.episode = 1 THEN T2.votes ELSE 0 END) - SUM(CASE WHEN T1.episode = 5 THEN T2.votes ELSE 0 END) AS diff FROM Episode AS T1 INNER JOIN Vote AS T2 ON T2.episode_id = T1.episode_id;
[ "How", "much", "more", "votes", "for", "episode", "1", "than", "for", "episode", "5", "?" ]
[ { "id": 2, "type": "column", "value": "episode_id" }, { "id": 0, "type": "table", "value": "episode" }, { "id": 5, "type": "column", "value": "episode" }, { "id": 4, "type": "column", "value": "votes" }, { "id": 1, "type": "table", "value": "vote" }, { "id": 3, "type": "value", "value": "0" }, { "id": 6, "type": "value", "value": "1" }, { "id": 7, "type": "value", "value": "5" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 3 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [ 9 ] }, { "entity_id": 6, "token_idxs": [ 6 ] }, { "entity_id": 7, "token_idxs": [ 10 ] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-TABLE", "O", "O", "B-VALUE", "O", "O", "B-COLUMN", "B-VALUE", "O" ]
14,963
customers_and_addresses
spider:train_spider.json:6067
What are the names of customers using the most popular payment method?
SELECT customer_name FROM customers WHERE payment_method = (SELECT payment_method FROM customers GROUP BY payment_method ORDER BY count(*) DESC LIMIT 1)
[ "What", "are", "the", "names", "of", "customers", "using", "the", "most", "popular", "payment", "method", "?" ]
[ { "id": 2, "type": "column", "value": "payment_method" }, { "id": 1, "type": "column", "value": "customer_name" }, { "id": 0, "type": "table", "value": "customers" } ]
[ { "entity_id": 0, "token_idxs": [ 5 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 10, 11 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O" ]
14,964
college_3
spider:train_spider.json:4684
Give the names of the courses with at least five enrollments.
SELECT T1.CName FROM COURSE AS T1 JOIN ENROLLED_IN AS T2 ON T1.CID = T2.CID GROUP BY T2.CID HAVING COUNT(*) >= 5
[ "Give", "the", "names", "of", "the", "courses", "with", "at", "least", "five", "enrollments", "." ]
[ { "id": 3, "type": "table", "value": "enrolled_in" }, { "id": 2, "type": "table", "value": "course" }, { "id": 1, "type": "column", "value": "cname" }, { "id": 0, "type": "column", "value": "cid" }, { "id": 4, "type": "value", "value": "5" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 2 ] }, { "entity_id": 2, "token_idxs": [ 5 ] }, { "entity_id": 3, "token_idxs": [ 10 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "O", "O", "B-TABLE", "O" ]
14,965
university
bird:train.json:7992
What is the ID of the university that has only 1% of international students between 2011 to 2015?
SELECT university_id FROM university_year WHERE pct_international_students = 1 AND year BETWEEN 2011 AND 2015
[ "What", "is", "the", "ID", "of", "the", "university", "that", "has", "only", "1", "%", "of", "international", "students", "between", "2011", "to", "2015", "?" ]
[ { "id": 2, "type": "column", "value": "pct_international_students" }, { "id": 0, "type": "table", "value": "university_year" }, { "id": 1, "type": "column", "value": "university_id" }, { "id": 4, "type": "column", "value": "year" }, { "id": 5, "type": "value", "value": "2011" }, { "id": 6, "type": "value", "value": "2015" }, { "id": 3, "type": "value", "value": "1" } ]
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[ "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "O", "B-VALUE", "O", "O", "B-COLUMN", "I-COLUMN", "O", "B-VALUE", "O", "B-VALUE", "O" ]
14,966
cre_Students_Information_Systems
bird:test.json:496
Find the biographical information of the student with the smallest student loan.
SELECT T1.bio_data FROM Students AS T1 JOIN Student_Loans AS T2 ON T1.student_id = T2.student_id ORDER BY T2.amount_of_loan ASC LIMIT 1
[ "Find", "the", "biographical", "information", "of", "the", "student", "with", "the", "smallest", "student", "loan", "." ]
[ { "id": 3, "type": "column", "value": "amount_of_loan" }, { "id": 2, "type": "table", "value": "student_loans" }, { "id": 4, "type": "column", "value": "student_id" }, { "id": 0, "type": "column", "value": "bio_data" }, { "id": 1, "type": "table", "value": "students" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 10 ] }, { "entity_id": 2, "token_idxs": [ 11 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "B-TABLE", "O" ]
14,967
dorm_1
spider:train_spider.json:5763
What are the first and last names of all students who are living in a dorm with a TV Lounge?
SELECT T1.fname , T1.lname FROM student AS T1 JOIN lives_in AS T2 ON T1.stuid = T2.stuid WHERE T2.dormid IN (SELECT T3.dormid FROM has_amenity AS T3 JOIN dorm_amenity AS T4 ON T3.amenid = T4.amenid WHERE T4.amenity_name = 'TV Lounge')
[ "What", "are", "the", "first", "and", "last", "names", "of", "all", "students", "who", "are", "living", "in", "a", "dorm", "with", "a", "TV", "Lounge", "?" ]
[ { "id": 7, "type": "table", "value": "dorm_amenity" }, { "id": 8, "type": "column", "value": "amenity_name" }, { "id": 6, "type": "table", "value": "has_amenity" }, { "id": 9, "type": "value", "value": "TV Lounge" }, { "id": 3, "type": "table", "value": "lives_in" }, { "id": 2, "type": "table", "value": "student" }, { "id": 4, "type": "column", "value": "dormid" }, { "id": 10, "type": "column", "value": "amenid" }, { "id": 0, "type": "column", "value": "fname" }, { "id": 1, "type": "column", "value": "lname" }, { "id": 5, "type": "column", "value": "stuid" } ]
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[ "O", "O", "O", "O", "B-COLUMN", "O", "B-COLUMN", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "B-COLUMN", "O", "O", "B-VALUE", "I-VALUE", "O" ]
14,968
university
bird:train.json:8041
In Argentina, how many universities are there?
SELECT COUNT(*) FROM university AS T1 INNER JOIN country AS T2 ON T1.country_id = T2.id WHERE T2.country_name = 'Argentina'
[ "In", "Argentina", ",", "how", "many", "universities", "are", "there", "?" ]
[ { "id": 2, "type": "column", "value": "country_name" }, { "id": 0, "type": "table", "value": "university" }, { "id": 4, "type": "column", "value": "country_id" }, { "id": 3, "type": "value", "value": "Argentina" }, { "id": 1, "type": "table", "value": "country" }, { "id": 5, "type": "column", "value": "id" } ]
[ { "entity_id": 0, "token_idxs": [ 5 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 1 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "B-VALUE", "O", "O", "O", "B-TABLE", "O", "O", "O" ]
14,969
soccer_2
spider:train_spider.json:5026
Find the names of schools that have some students playing in goalie and mid positions.
SELECT cName FROM tryout WHERE pPos = 'goalie' INTERSECT SELECT cName FROM tryout WHERE pPos = 'mid'
[ "Find", "the", "names", "of", "schools", "that", "have", "some", "students", "playing", "in", "goalie", "and", "mid", "positions", "." ]
[ { "id": 0, "type": "table", "value": "tryout" }, { "id": 3, "type": "value", "value": "goalie" }, { "id": 1, "type": "column", "value": "cname" }, { "id": 2, "type": "column", "value": "ppos" }, { "id": 4, "type": "value", "value": "mid" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 2 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 11 ] }, { "entity_id": 4, "token_idxs": [ 13 ] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-COLUMN", "O", "O", "O", "O", "O", "O", "O", "O", "B-VALUE", "O", "B-VALUE", "O", "O" ]
14,970
car_retails
bird:train.json:1622
For the product No. S18_3482 in the Order No.10108, how much discount did the customer have?
SELECT (t1.MSRP - t2.priceEach) / t1.MSRP FROM products AS t1 INNER JOIN orderdetails AS t2 ON t1.productCode = t2.productCode WHERE t1.productCode = 'S18_3482' AND t2.orderNumber = '10108'
[ "For", "the", "product", "No", ".", "S18_3482", "in", "the", "Order", "No.10108", ",", "how", "much", "discount", "did", "the", "customer", "have", "?" ]
[ { "id": 1, "type": "table", "value": "orderdetails" }, { "id": 3, "type": "column", "value": "productcode" }, { "id": 5, "type": "column", "value": "ordernumber" }, { "id": 7, "type": "column", "value": "priceeach" }, { "id": 0, "type": "table", "value": "products" }, { "id": 4, "type": "value", "value": "S18_3482" }, { "id": 6, "type": "value", "value": "10108" }, { "id": 2, "type": "column", "value": "msrp" } ]
[ { "entity_id": 0, "token_idxs": [ 2 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [ 5 ] }, { "entity_id": 5, "token_idxs": [ 8 ] }, { "entity_id": 6, "token_idxs": [ 9 ] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-TABLE", "O", "O", "B-VALUE", "O", "O", "B-COLUMN", "B-VALUE", "O", "O", "O", "O", "O", "O", "O", "O", "O" ]
14,971
protein_institute
spider:train_spider.json:1913
What are the average, maximum, and minimum number of floors for all buildings?
SELECT avg(floors) , max(floors) , min(floors) FROM building
[ "What", "are", "the", "average", ",", "maximum", ",", "and", "minimum", "number", "of", "floors", "for", "all", "buildings", "?" ]
[ { "id": 0, "type": "table", "value": "building" }, { "id": 1, "type": "column", "value": "floors" } ]
[ { "entity_id": 0, "token_idxs": [ 14 ] }, { "entity_id": 1, "token_idxs": [ 11 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O" ]
14,972
works_cycles
bird:train.json:7184
What is the name of product purchased with transaction type P?
SELECT ProductID FROM Product WHERE ProductID IN ( SELECT ProductID FROM TransactionHistory WHERE TransactionType = 'P' )
[ "What", "is", "the", "name", "of", "product", "purchased", "with", "transaction", "type", "P", "?" ]
[ { "id": 2, "type": "table", "value": "transactionhistory" }, { "id": 3, "type": "column", "value": "transactiontype" }, { "id": 1, "type": "column", "value": "productid" }, { "id": 0, "type": "table", "value": "product" }, { "id": 4, "type": "value", "value": "P" } ]
[ { "entity_id": 0, "token_idxs": [ 5 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 8, 9 ] }, { "entity_id": 4, "token_idxs": [ 10 ] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "B-COLUMN", "I-COLUMN", "B-VALUE", "O" ]
14,973
menu
bird:train.json:5475
Please list the prices of the dish "Clear green turtle" on every menu page it appeared on.
SELECT T2.price FROM Dish AS T1 INNER JOIN MenuItem AS T2 ON T1.id = T2.dish_id WHERE T1.name = 'Clear green turtle'
[ "Please", "list", "the", "prices", "of", "the", "dish", "\"", "Clear", "green", "turtle", "\"", "on", "every", "menu", "page", "it", "appeared", "on", "." ]
[ { "id": 4, "type": "value", "value": "Clear green turtle" }, { "id": 2, "type": "table", "value": "menuitem" }, { "id": 6, "type": "column", "value": "dish_id" }, { "id": 0, "type": "column", "value": "price" }, { "id": 1, "type": "table", "value": "dish" }, { "id": 3, "type": "column", "value": "name" }, { "id": 5, "type": "column", "value": "id" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "B-VALUE", "I-VALUE", "I-VALUE", "O", "O", "O", "B-TABLE", "O", "O", "O", "O", "O" ]
14,974
voter_2
spider:train_spider.json:5496
Find the distinct last names of all the students who have president votes and whose advisor is not 2192.
SELECT DISTINCT T1.LName FROM STUDENT AS T1 JOIN VOTING_RECORD AS T2 ON T1.StuID = PRESIDENT_Vote EXCEPT SELECT DISTINCT LName FROM STUDENT WHERE Advisor = "2192"
[ "Find", "the", "distinct", "last", "names", "of", "all", "the", "students", "who", "have", "president", "votes", "and", "whose", "advisor", "is", "not", "2192", "." ]
[ { "id": 6, "type": "column", "value": "president_vote" }, { "id": 2, "type": "table", "value": "voting_record" }, { "id": 0, "type": "table", "value": "student" }, { "id": 3, "type": "column", "value": "advisor" }, { "id": 1, "type": "column", "value": "lname" }, { "id": 5, "type": "column", "value": "stuid" }, { "id": 4, "type": "column", "value": "2192" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 4 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 15 ] }, { "entity_id": 4, "token_idxs": [ 18 ] }, { "entity_id": 5, "token_idxs": [ 8 ] }, { "entity_id": 6, "token_idxs": [ 11, 12 ] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "B-COLUMN", "O", "O", "O", "B-COLUMN", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "B-COLUMN", "O", "O", "B-COLUMN", "O" ]
14,975
bike_share_1
bird:train.json:8996
What was the hottest temperature on the day of trip ID 4080?
SELECT MAX(T2.max_temperature_f) FROM trip AS T1 INNER JOIN weather AS T2 ON T2.zip_code = T1.zip_code AND T2.date = SUBSTR(CAST(T1.start_date AS TEXT), 1, INSTR(T1.start_date, ' ') - 1) WHERE T1.id = 4080
[ "What", "was", "the", "hottest", "temperature", "on", "the", "day", "of", "trip", "ID", "4080", "?" ]
[ { "id": 4, "type": "column", "value": "max_temperature_f" }, { "id": 8, "type": "column", "value": "start_date" }, { "id": 5, "type": "column", "value": "zip_code" }, { "id": 1, "type": "table", "value": "weather" }, { "id": 0, "type": "table", "value": "trip" }, { "id": 3, "type": "value", "value": "4080" }, { "id": 6, "type": "column", "value": "date" }, { "id": 2, "type": "column", "value": "id" }, { "id": 7, "type": "value", "value": "1" }, { "id": 9, "type": "value", "value": " " } ]
[ { "entity_id": 0, "token_idxs": [ 9 ] }, { "entity_id": 1, "token_idxs": [ 1, 2 ] }, { "entity_id": 2, "token_idxs": [ 10 ] }, { "entity_id": 3, "token_idxs": [ 11 ] }, { "entity_id": 4, "token_idxs": [ 4 ] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "B-TABLE", "I-TABLE", "O", "B-COLUMN", "O", "O", "O", "O", "B-TABLE", "B-COLUMN", "B-VALUE", "O" ]
14,976
flight_1
spider:train_spider.json:410
What destination has the fewest number of flights?
SELECT destination FROM Flight GROUP BY destination ORDER BY count(*) LIMIT 1
[ "What", "destination", "has", "the", "fewest", "number", "of", "flights", "?" ]
[ { "id": 1, "type": "column", "value": "destination" }, { "id": 0, "type": "table", "value": "flight" } ]
[ { "entity_id": 0, "token_idxs": [ 7 ] }, { "entity_id": 1, "token_idxs": [ 1 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "B-COLUMN", "O", "O", "O", "O", "O", "B-TABLE", "O" ]
14,977
chicago_crime
bird:train.json:8633
How many neighborhoods are there in Near North Side?
SELECT SUM(CASE WHEN T1.community_area_name = 'Near North Side' THEN 1 ELSE 0 END) FROM Community_Area AS T1 INNER JOIN Neighborhood AS T2 ON T1.community_area_no = T2.community_area_no
[ "How", "many", "neighborhoods", "are", "there", "in", "Near", "North", "Side", "?" ]
[ { "id": 5, "type": "column", "value": "community_area_name" }, { "id": 2, "type": "column", "value": "community_area_no" }, { "id": 6, "type": "value", "value": "Near North Side" }, { "id": 0, "type": "table", "value": "community_area" }, { "id": 1, "type": "table", "value": "neighborhood" }, { "id": 3, "type": "value", "value": "0" }, { "id": 4, "type": "value", "value": "1" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 2 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [ 6, 7, 8 ] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-TABLE", "O", "O", "O", "B-VALUE", "I-VALUE", "I-VALUE", "O" ]
14,978
cre_Students_Information_Systems
bird:test.json:473
List the date of the transcripts and the transcript details.
SELECT date_of_transcript , transcript_details FROM Transcripts
[ "List", "the", "date", "of", "the", "transcripts", "and", "the", "transcript", "details", "." ]
[ { "id": 1, "type": "column", "value": "date_of_transcript" }, { "id": 2, "type": "column", "value": "transcript_details" }, { "id": 0, "type": "table", "value": "transcripts" } ]
[ { "entity_id": 0, "token_idxs": [ 5 ] }, { "entity_id": 1, "token_idxs": [ 2, 3, 4 ] }, { "entity_id": 2, "token_idxs": [ 8, 9 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "B-TABLE", "O", "O", "B-COLUMN", "I-COLUMN", "O" ]
14,979
aan_1
bird:test.json:963
How many authors do we have?
SELECT count(*) FROM Author
[ "How", "many", "authors", "do", "we", "have", "?" ]
[ { "id": 0, "type": "table", "value": "author" } ]
[ { "entity_id": 0, "token_idxs": [ 2 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-TABLE", "O", "O", "O", "O" ]
14,980
activity_1
spider:train_spider.json:6723
Show the first name, last name, and phone number for all female faculty members.
SELECT Fname , Lname , phone FROM Faculty WHERE Sex = 'F'
[ "Show", "the", "first", "name", ",", "last", "name", ",", "and", "phone", "number", "for", "all", "female", "faculty", "members", "." ]
[ { "id": 0, "type": "table", "value": "faculty" }, { "id": 1, "type": "column", "value": "fname" }, { "id": 2, "type": "column", "value": "lname" }, { "id": 3, "type": "column", "value": "phone" }, { "id": 4, "type": "column", "value": "sex" }, { "id": 5, "type": "value", "value": "F" } ]
[ { "entity_id": 0, "token_idxs": [ 14 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 6 ] }, { "entity_id": 3, "token_idxs": [ 9 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "B-COLUMN", "O", "O", "O", "O", "B-TABLE", "O", "O" ]
14,981
law_episode
bird:train.json:1304
What are the names of the two people who won an award for their role as directors?
SELECT T1.name FROM Person AS T1 INNER JOIN Award AS T2 ON T1.person_id = T2.person_id WHERE T2.Result = 'Winner' AND T2.role = 'director'
[ "What", "are", "the", "names", "of", "the", "two", "people", "who", "won", "an", "award", "for", "their", "role", "as", "directors", "?" ]
[ { "id": 3, "type": "column", "value": "person_id" }, { "id": 7, "type": "value", "value": "director" }, { "id": 1, "type": "table", "value": "person" }, { "id": 4, "type": "column", "value": "result" }, { "id": 5, "type": "value", "value": "Winner" }, { "id": 2, "type": "table", "value": "award" }, { "id": 0, "type": "column", "value": "name" }, { "id": 6, "type": "column", "value": "role" } ]
[ { "entity_id": 0, "token_idxs": [ 3 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 11 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [ 14 ] }, { "entity_id": 7, "token_idxs": [ 16 ] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-COLUMN", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "B-COLUMN", "O", "B-VALUE", "O" ]
14,982
mondial_geo
bird:train.json:8428
What is the average population ratio of the countries in which organizations were established in 1947?
SELECT T1.Population / T1.Area FROM country AS T1 INNER JOIN organization AS T2 ON T1.Code = T2.Country WHERE STRFTIME('%Y', T2.Established) = '1947'
[ "What", "is", "the", "average", "population", "ratio", "of", "the", "countries", "in", "which", "organizations", "were", "established", "in", "1947", "?" ]
[ { "id": 1, "type": "table", "value": "organization" }, { "id": 8, "type": "column", "value": "established" }, { "id": 3, "type": "column", "value": "population" }, { "id": 0, "type": "table", "value": "country" }, { "id": 6, "type": "column", "value": "country" }, { "id": 2, "type": "value", "value": "1947" }, { "id": 4, "type": "column", "value": "area" }, { "id": 5, "type": "column", "value": "code" }, { "id": 7, "type": "value", "value": "%Y" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 11 ] }, { "entity_id": 2, "token_idxs": [ 15 ] }, { "entity_id": 3, "token_idxs": [ 4 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [ 8 ] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [ 13 ] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "B-COLUMN", "O", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "B-COLUMN", "O", "B-VALUE", "O" ]
14,984
regional_sales
bird:train.json:2599
Describe the customer names and product names which had over 3800 USD in net profit.
SELECT DISTINCT `Customer Names`, `Product Name` FROM ( SELECT T1.`Customer Names`, T3.`Product Name` , REPLACE(T2.`Unit Price`, ',', '') - REPLACE(T2.`Unit Cost`, ',', '') AS T FROM Customers T1 INNER JOIN `Sales Orders` T2 ON T2._CustomerID = T1.CustomerID INNER JOIN Products T3 ON T3.ProductID = T2._ProductID ) WHERE T > 3800
[ "Describe", "the", "customer", "names", "and", "product", "names", "which", "had", "over", "3800", "USD", "in", "net", "profit", "." ]
[ { "id": 0, "type": "column", "value": "Customer Names" }, { "id": 1, "type": "column", "value": "Product Name" }, { "id": 6, "type": "table", "value": "Sales Orders" }, { "id": 12, "type": "column", "value": "_customerid" }, { "id": 8, "type": "column", "value": "_productid" }, { "id": 9, "type": "column", "value": "Unit Price" }, { "id": 13, "type": "column", "value": "customerid" }, { "id": 5, "type": "table", "value": "customers" }, { "id": 7, "type": "column", "value": "productid" }, { "id": 11, "type": "column", "value": "Unit Cost" }, { "id": 4, "type": "table", "value": "products" }, { "id": 3, "type": "value", "value": "3800" }, { "id": 2, "type": "column", "value": "t" }, { "id": 10, "type": "value", "value": "," } ]
[ { "entity_id": 0, "token_idxs": [ 3 ] }, { "entity_id": 1, "token_idxs": [ 6 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 10 ] }, { "entity_id": 4, "token_idxs": [ 5 ] }, { "entity_id": 5, "token_idxs": [ 2 ] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [ 13, 14 ] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-TABLE", "B-COLUMN", "O", "B-TABLE", "B-COLUMN", "O", "O", "O", "B-VALUE", "O", "O", "B-COLUMN", "I-COLUMN", "O" ]
14,985
wrestler
spider:train_spider.json:1846
List the names of wrestlers in descending order of days held.
SELECT Name FROM wrestler ORDER BY Days_held DESC
[ "List", "the", "names", "of", "wrestlers", "in", "descending", "order", "of", "days", "held", "." ]
[ { "id": 2, "type": "column", "value": "days_held" }, { "id": 0, "type": "table", "value": "wrestler" }, { "id": 1, "type": "column", "value": "name" } ]
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[ "O", "O", "B-COLUMN", "O", "B-TABLE", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O" ]
14,986
app_store
bird:train.json:2573
Among the role playing game genre, how many are targeted to teens and what is their average sentiment polarity score?
SELECT COUNT(T1.App), AVG(T2.Sentiment_Polarity) FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1."Content Rating" = 'Teen' AND T1.Genres = 'Role Playing'
[ "Among", "the", "role", "playing", "game", "genre", ",", "how", "many", "are", "targeted", "to", "teens", "and", "what", "is", "their", "average", "sentiment", "polarity", "score", "?" ]
[ { "id": 3, "type": "column", "value": "sentiment_polarity" }, { "id": 4, "type": "column", "value": "Content Rating" }, { "id": 1, "type": "table", "value": "user_reviews" }, { "id": 7, "type": "value", "value": "Role Playing" }, { "id": 0, "type": "table", "value": "playstore" }, { "id": 6, "type": "column", "value": "genres" }, { "id": 5, "type": "value", "value": "Teen" }, { "id": 2, "type": "column", "value": "app" } ]
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[ "O", "O", "B-VALUE", "I-VALUE", "O", "B-COLUMN", "O", "O", "O", "O", "O", "O", "B-VALUE", "O", "O", "O", "O", "O", "B-COLUMN", "B-TABLE", "I-TABLE", "O" ]
14,987
donor
bird:train.json:3186
List the title of all projects located in Chicago along with the ID of the donor.
SELECT T1.title, T3.donor_acctid FROM essays AS T1 INNER JOIN projects AS T2 ON T1.projectid = T2.projectid INNER JOIN donations AS T3 ON T2.projectid = T3.projectid WHERE T2.school_city LIKE 'Chicago'
[ "List", "the", "title", "of", "all", "projects", "located", "in", "Chicago", "along", "with", "the", "ID", "of", "the", "donor", "." ]
[ { "id": 1, "type": "column", "value": "donor_acctid" }, { "id": 3, "type": "column", "value": "school_city" }, { "id": 2, "type": "table", "value": "donations" }, { "id": 7, "type": "column", "value": "projectid" }, { "id": 6, "type": "table", "value": "projects" }, { "id": 4, "type": "value", "value": "Chicago" }, { "id": 5, "type": "table", "value": "essays" }, { "id": 0, "type": "column", "value": "title" } ]
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[ "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "B-VALUE", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O" ]
14,988
legislator
bird:train.json:4831
Who is the Pro-Administration senator that runs from March 4, 1789 to December 31, 1791?
SELECT T1.first_name, T1.last_name FROM historical AS T1 INNER JOIN `historical-terms` AS T2 ON T1.bioguide_id = T2.bioguide WHERE T2.party = 'Pro-Administration' AND T2.start = '1789-03-04' AND T2.end = '1791-12-31'
[ "Who", "is", "the", "Pro", "-", "Administration", "senator", "that", "runs", "from", "March", "4", ",", "1789", "to", "December", "31", ",", "1791", "?" ]
[ { "id": 7, "type": "value", "value": "Pro-Administration" }, { "id": 3, "type": "table", "value": "historical-terms" }, { "id": 4, "type": "column", "value": "bioguide_id" }, { "id": 0, "type": "column", "value": "first_name" }, { "id": 2, "type": "table", "value": "historical" }, { "id": 9, "type": "value", "value": "1789-03-04" }, { "id": 11, "type": "value", "value": "1791-12-31" }, { "id": 1, "type": "column", "value": "last_name" }, { "id": 5, "type": "column", "value": "bioguide" }, { "id": 6, "type": "column", "value": "party" }, { "id": 8, "type": "column", "value": "start" }, { "id": 10, "type": "column", "value": "end" } ]
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[ "O", "O", "O", "B-VALUE", "I-VALUE", "I-VALUE", "O", "B-COLUMN", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O" ]