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15,529
gas_company
spider:train_spider.json:2010
For each main industry, what is the total number of companies for the industry with the highest total market value?
SELECT main_industry , count(*) FROM company GROUP BY main_industry ORDER BY sum(market_value) DESC LIMIT 1
[ "For", "each", "main", "industry", ",", "what", "is", "the", "total", "number", "of", "companies", "for", "the", "industry", "with", "the", "highest", "total", "market", "value", "?" ]
[ { "id": 1, "type": "column", "value": "main_industry" }, { "id": 2, "type": "column", "value": "market_value" }, { "id": 0, "type": "table", "value": "company" } ]
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[ "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O" ]
15,530
book_press
bird:test.json:2011
find the name and gender of the author who published the most books.
SELECT t1.name , t1.gender FROM author AS t1 JOIN book AS t2 ON t1.author_id = t2.author_id GROUP BY t2.author_id ORDER BY count(*) DESC LIMIT 1
[ "find", "the", "name", "and", "gender", "of", "the", "author", "who", "published", "the", "most", "books", "." ]
[ { "id": 0, "type": "column", "value": "author_id" }, { "id": 2, "type": "column", "value": "gender" }, { "id": 3, "type": "table", "value": "author" }, { "id": 1, "type": "column", "value": "name" }, { "id": 4, "type": "table", "value": "book" } ]
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[ "O", "O", "B-COLUMN", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "O", "O", "B-TABLE", "O" ]
15,531
world_development_indicators
bird:train.json:2105
What are the special notes for the country whose average adolescent fertility rate is the highest?
SELECT DISTINCT T1.SpecialNotes FROM Country AS T1 INNER JOIN Indicators AS T2 ON T1.CountryCode = T2.CountryCode WHERE T2.Value = ( SELECT Value FROM Indicators WHERE IndicatorName LIKE 'Adolescent fertility rate%' ORDER BY Value DESC LIMIT 1 )
[ "What", "are", "the", "special", "notes", "for", "the", "country", "whose", "average", "adolescent", "fertility", "rate", "is", "the", "highest", "?" ]
[ { "id": 6, "type": "value", "value": "Adolescent fertility rate%" }, { "id": 5, "type": "column", "value": "indicatorname" }, { "id": 0, "type": "column", "value": "specialnotes" }, { "id": 4, "type": "column", "value": "countrycode" }, { "id": 2, "type": "table", "value": "indicators" }, { "id": 1, "type": "table", "value": "country" }, { "id": 3, "type": "column", "value": "value" } ]
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[ "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "B-TABLE", "O", "O", "B-VALUE", "I-VALUE", "I-VALUE", "O", "O", "O", "O" ]
15,532
products_gen_characteristics
spider:train_spider.json:5555
Count the number of characteristics the product 'sesame' has.
SELECT count(*) FROM products AS t1 JOIN product_characteristics AS t2 ON t1.product_id = t2.product_id WHERE t1.product_name = "sesame"
[ "Count", "the", "number", "of", "characteristics", "the", "product", "'", "sesame", "'", "has", "." ]
[ { "id": 1, "type": "table", "value": "product_characteristics" }, { "id": 2, "type": "column", "value": "product_name" }, { "id": 4, "type": "column", "value": "product_id" }, { "id": 0, "type": "table", "value": "products" }, { "id": 3, "type": "column", "value": "sesame" } ]
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[ "O", "O", "O", "O", "B-TABLE", "O", "B-TABLE", "B-COLUMN", "B-COLUMN", "O", "O", "O" ]
15,533
movie_1
spider:train_spider.json:2466
What is the maximum stars and year for the most recent movie?
SELECT max(T1.stars) , T2.year FROM Rating AS T1 JOIN Movie AS T2 ON T1.mID = T2.mID WHERE T2.year = (SELECT max(YEAR) FROM Movie)
[ "What", "is", "the", "maximum", "stars", "and", "year", "for", "the", "most", "recent", "movie", "?" ]
[ { "id": 1, "type": "table", "value": "rating" }, { "id": 2, "type": "table", "value": "movie" }, { "id": 3, "type": "column", "value": "stars" }, { "id": 0, "type": "column", "value": "year" }, { "id": 4, "type": "column", "value": "mid" } ]
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[ "O", "O", "O", "O", "B-COLUMN", "O", "B-COLUMN", "O", "O", "O", "O", "B-TABLE", "O" ]
15,534
olympics
bird:train.json:5029
Please list all competitors' names who participated in 1936 Summer.
SELECT T3.full_name FROM games AS T1 INNER JOIN games_competitor AS T2 ON T1.id = T2.games_id INNER JOIN person AS T3 ON T2.person_id = T3.id WHERE T1.games_name = '1936 Summer'
[ "Please", "list", "all", "competitors", "'", "names", "who", "participated", "in", "1936", "Summer", "." ]
[ { "id": 5, "type": "table", "value": "games_competitor" }, { "id": 3, "type": "value", "value": "1936 Summer" }, { "id": 2, "type": "column", "value": "games_name" }, { "id": 0, "type": "column", "value": "full_name" }, { "id": 6, "type": "column", "value": "person_id" }, { "id": 8, "type": "column", "value": "games_id" }, { "id": 1, "type": "table", "value": "person" }, { "id": 4, "type": "table", "value": "games" }, { "id": 7, "type": "column", "value": "id" } ]
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[ "O", "O", "O", "B-TABLE", "O", "B-TABLE", "O", "O", "O", "B-VALUE", "I-VALUE", "O" ]
15,535
school_bus
spider:train_spider.json:6350
Show the party and the number of drivers in each party.
SELECT party , count(*) FROM driver GROUP BY party
[ "Show", "the", "party", "and", "the", "number", "of", "drivers", "in", "each", "party", "." ]
[ { "id": 0, "type": "table", "value": "driver" }, { "id": 1, "type": "column", "value": "party" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "B-COLUMN", "O" ]
15,536
social_media
bird:train.json:823
What gender of users posted the most tweets in `en`?
SELECT T.Gender FROM ( SELECT T2.Gender, COUNT( text) AS num FROM twitter AS T1 INNER JOIN user AS T2 ON T1.UserID = T2.UserID WHERE T1.Lang = 'en' GROUP BY T2.Gender ) T ORDER BY T.num DESC LIMIT 1
[ "What", "gender", "of", "users", "posted", "the", "most", "tweets", "in", "`", "en", "`", "?" ]
[ { "id": 2, "type": "table", "value": "twitter" }, { "id": 0, "type": "column", "value": "gender" }, { "id": 6, "type": "column", "value": "userid" }, { "id": 3, "type": "column", "value": "lang" }, { "id": 5, "type": "column", "value": "text" }, { "id": 1, "type": "column", "value": "num" }, { "id": 4, "type": "value", "value": "en" } ]
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[ "O", "B-COLUMN", "O", "B-COLUMN", "O", "O", "O", "B-COLUMN", "O", "O", "B-VALUE", "O", "O" ]
15,537
synthea
bird:train.json:1531
Indicate the care plan needed for the patient living at 179 Sydni Roads, Taunton, MA 02780 US.
SELECT T1.DESCRIPTION FROM careplans AS T1 INNER JOIN patients AS T2 ON T1.PATIENT = T2.patient WHERE T2.address = '179 Sydni Roads Taunton MA 02780 US'
[ "Indicate", "the", "care", "plan", "needed", "for", "the", "patient", "living", "at", "179", "Sydni", "Roads", ",", "Taunton", ",", "MA", "02780", "US", "." ]
[ { "id": 4, "type": "value", "value": "179 Sydni Roads Taunton MA 02780 US" }, { "id": 0, "type": "column", "value": "description" }, { "id": 1, "type": "table", "value": "careplans" }, { "id": 2, "type": "table", "value": "patients" }, { "id": 3, "type": "column", "value": "address" }, { "id": 5, "type": "column", "value": "patient" } ]
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[ "O", "O", "B-TABLE", "I-TABLE", "O", "O", "O", "B-COLUMN", "O", "O", "B-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "O" ]
15,538
talkingdata
bird:train.json:1214
Locate all events on devices of women under 30 years old.
SELECT T1.device_id FROM gender_age AS T1 INNER JOIN events_relevant AS T2 ON T1.device_id = T2.device_id WHERE T1.gender = 'F' AND T1.age < 30
[ "Locate", "all", "events", "on", "devices", "of", "women", "under", "30", "years", "old", "." ]
[ { "id": 2, "type": "table", "value": "events_relevant" }, { "id": 1, "type": "table", "value": "gender_age" }, { "id": 0, "type": "column", "value": "device_id" }, { "id": 3, "type": "column", "value": "gender" }, { "id": 5, "type": "column", "value": "age" }, { "id": 6, "type": "value", "value": "30" }, { "id": 4, "type": "value", "value": "F" } ]
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[ "O", "O", "B-TABLE", "I-TABLE", "B-COLUMN", "B-VALUE", "O", "B-COLUMN", "B-VALUE", "O", "O", "O" ]
15,539
movies_4
bird:train.json:504
List down the IDs of the production companies that released the movies in 1916.
SELECT T2.company_id FROM movie AS T1 INNER JOIN movie_company AS T2 ON T1.movie_id = T2.movie_id WHERE CAST(STRFTIME('%Y', T1.release_date) AS INT) = 1916
[ "List", "down", "the", "IDs", "of", "the", "production", "companies", "that", "released", "the", "movies", "in", "1916", "." ]
[ { "id": 2, "type": "table", "value": "movie_company" }, { "id": 6, "type": "column", "value": "release_date" }, { "id": 0, "type": "column", "value": "company_id" }, { "id": 4, "type": "column", "value": "movie_id" }, { "id": 1, "type": "table", "value": "movie" }, { "id": 3, "type": "value", "value": "1916" }, { "id": 5, "type": "value", "value": "%Y" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "B-COLUMN", "I-COLUMN", "B-TABLE", "B-COLUMN", "B-VALUE", "O" ]
15,540
mountain_photos
spider:train_spider.json:3721
How many different kinds of lens brands are there?
SELECT count(DISTINCT brand) FROM camera_lens
[ "How", "many", "different", "kinds", "of", "lens", "brands", "are", "there", "?" ]
[ { "id": 0, "type": "table", "value": "camera_lens" }, { "id": 1, "type": "column", "value": "brand" } ]
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[ "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "O" ]
15,541
superhero
bird:dev.json:760
In superheroes with height between 150 to 180, what is the percentage of heroes published by Marvel Comics?
SELECT CAST(COUNT(CASE WHEN T2.publisher_name = 'Marvel Comics' THEN 1 ELSE NULL END) AS REAL) * 100 / COUNT(T1.id) FROM superhero AS T1 INNER JOIN publisher AS T2 ON T1.publisher_id = T2.id WHERE T1.height_cm BETWEEN 150 AND 180
[ "In", "superheroes", "with", "height", "between", "150", "to", "180", ",", "what", "is", "the", "percentage", "of", "heroes", "published", "by", "Marvel", "Comics", "?" ]
[ { "id": 9, "type": "column", "value": "publisher_name" }, { "id": 10, "type": "value", "value": "Marvel Comics" }, { "id": 5, "type": "column", "value": "publisher_id" }, { "id": 0, "type": "table", "value": "superhero" }, { "id": 1, "type": "table", "value": "publisher" }, { "id": 2, "type": "column", "value": "height_cm" }, { "id": 3, "type": "value", "value": "150" }, { "id": 4, "type": "value", "value": "180" }, { "id": 7, "type": "value", "value": "100" }, { "id": 6, "type": "column", "value": "id" }, { "id": 8, "type": "value", "value": "1" } ]
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[ "O", "B-TABLE", "O", "B-COLUMN", "O", "B-VALUE", "O", "B-VALUE", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "B-VALUE", "I-VALUE", "O" ]
15,542
retail_world
bird:train.json:6530
What was the total amount of sales handled by Nancy Davolio in December 1996, excluding discounts?
SELECT SUM(T3.UnitPrice * T3.Quantity) FROM Employees AS T1 INNER JOIN Orders AS T2 ON T1.EmployeeID = T2.EmployeeID INNER JOIN `Order Details` AS T3 ON T2.OrderID = T3.OrderID WHERE T1.FirstName = 'Nancy' AND T1.LastName = 'Davolio' AND T2.OrderDate LIKE '1996-12%' AND T3.Discount = 0
[ "What", "was", "the", "total", "amount", "of", "sales", "handled", "by", "Nancy", "Davolio", "in", "December", "1996", ",", "excluding", "discounts", "?" ]
[ { "id": 0, "type": "table", "value": "Order Details" }, { "id": 14, "type": "column", "value": "employeeid" }, { "id": 1, "type": "table", "value": "employees" }, { "id": 4, "type": "column", "value": "firstname" }, { "id": 8, "type": "column", "value": "orderdate" }, { "id": 12, "type": "column", "value": "unitprice" }, { "id": 6, "type": "column", "value": "lastname" }, { "id": 9, "type": "value", "value": "1996-12%" }, { "id": 10, "type": "column", "value": "discount" }, { "id": 13, "type": "column", "value": "quantity" }, { "id": 3, "type": "column", "value": "orderid" }, { "id": 7, "type": "value", "value": "Davolio" }, { "id": 2, "type": "table", "value": "orders" }, { "id": 5, "type": "value", "value": "Nancy" }, { "id": 11, "type": "value", "value": "0" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-VALUE", "B-VALUE", "O", "O", "B-VALUE", "O", "O", "B-COLUMN", "O" ]
15,543
bakery_1
bird:test.json:1494
Give the ids of goods that cost less than 3 dollars.
SELECT id FROM goods WHERE price < 3
[ "Give", "the", "ids", "of", "goods", "that", "cost", "less", "than", "3", "dollars", "." ]
[ { "id": 0, "type": "table", "value": "goods" }, { "id": 2, "type": "column", "value": "price" }, { "id": 1, "type": "column", "value": "id" }, { "id": 3, "type": "value", "value": "3" } ]
[ { "entity_id": 0, "token_idxs": [ 4 ] }, { "entity_id": 1, "token_idxs": [ 2 ] }, { "entity_id": 2, "token_idxs": [] }, { "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", "B-COLUMN", "O", "B-TABLE", "O", "O", "O", "O", "B-VALUE", "O", "O" ]
15,544
superhero
bird:dev.json:722
What is the colour of Apocalypse's skin?
SELECT T2.colour FROM superhero AS T1 INNER JOIN colour AS T2 ON T1.skin_colour_id = T2.id WHERE T1.superhero_name = 'Apocalypse'
[ "What", "is", "the", "colour", "of", "Apocalypse", "'s", "skin", "?" ]
[ { "id": 3, "type": "column", "value": "superhero_name" }, { "id": 5, "type": "column", "value": "skin_colour_id" }, { "id": 4, "type": "value", "value": "Apocalypse" }, { "id": 1, "type": "table", "value": "superhero" }, { "id": 0, "type": "column", "value": "colour" }, { "id": 2, "type": "table", "value": "colour" }, { "id": 6, "type": "column", "value": "id" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 3 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [ 5 ] }, { "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-TABLE", "O", "B-VALUE", "O", "O", "O" ]
15,545
financial
bird:dev.json:148
Please list the accounts whose district is Tabor that are eligible for loans.
SELECT T2.account_id FROM district AS T1 INNER JOIN account AS T2 ON T1.district_id = T2.district_id INNER JOIN disp AS T3 ON T2.account_id = T3.account_id WHERE T3.type = 'OWNER' AND T1.A2 = 'Tabor'
[ "Please", "list", "the", "accounts", "whose", "district", "is", "Tabor", "that", "are", "eligible", "for", "loans", "." ]
[ { "id": 8, "type": "column", "value": "district_id" }, { "id": 0, "type": "column", "value": "account_id" }, { "id": 2, "type": "table", "value": "district" }, { "id": 3, "type": "table", "value": "account" }, { "id": 5, "type": "value", "value": "OWNER" }, { "id": 7, "type": "value", "value": "Tabor" }, { "id": 1, "type": "table", "value": "disp" }, { "id": 4, "type": "column", "value": "type" }, { "id": 6, "type": "column", "value": "a2" } ]
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[ "O", "O", "O", "B-TABLE", "O", "B-TABLE", "B-TABLE", "B-VALUE", "O", "O", "O", "O", "O", "O" ]
15,546
formula_1
spider:train_spider.json:2154
What is the name of the race that occurred most recently?
SELECT name FROM races ORDER BY date DESC LIMIT 1
[ "What", "is", "the", "name", "of", "the", "race", "that", "occurred", "most", "recently", "?" ]
[ { "id": 0, "type": "table", "value": "races" }, { "id": 1, "type": "column", "value": "name" }, { "id": 2, "type": "column", "value": "date" } ]
[ { "entity_id": 0, "token_idxs": [ 6 ] }, { "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": [] }, { "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", "B-TABLE", "O", "O", "O", "O", "O" ]
15,547
college_completion
bird:train.json:3709
Please list the names of the institutes in the state of Alabama whose all graduates in total exceeded 500 in 2011?
SELECT DISTINCT T1.chronname FROM institution_details AS T1 INNER JOIN institution_grads AS T2 ON T2.unitid = T1.unitid WHERE T1.state = 'Alabama' AND T2.year = 2011 AND T2.race = 'X' AND T2.grad_cohort > 500
[ "Please", "list", "the", "names", "of", "the", "institutes", "in", "the", "state", "of", "Alabama", "whose", "all", "graduates", "in", "total", "exceeded", "500", "in", "2011", "?" ]
[ { "id": 1, "type": "table", "value": "institution_details" }, { "id": 2, "type": "table", "value": "institution_grads" }, { "id": 10, "type": "column", "value": "grad_cohort" }, { "id": 0, "type": "column", "value": "chronname" }, { "id": 5, "type": "value", "value": "Alabama" }, { "id": 3, "type": "column", "value": "unitid" }, { "id": 4, "type": "column", "value": "state" }, { "id": 6, "type": "column", "value": "year" }, { "id": 7, "type": "value", "value": "2011" }, { "id": 8, "type": "column", "value": "race" }, { "id": 11, "type": "value", "value": "500" }, { "id": 9, "type": "value", "value": "X" } ]
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[ "O", "O", "O", "O", "O", "O", "B-TABLE", "I-TABLE", "O", "B-COLUMN", "O", "B-VALUE", "O", "O", "O", "O", "O", "O", "B-VALUE", "O", "B-VALUE", "O" ]
15,548
warehouse_1
bird:test.json:1745
Find the number of different locations where Rocks are stored.
SELECT count(DISTINCT LOCATION) FROM boxes AS T1 JOIN warehouses AS T2 ON T1.warehouse = T2.code WHERE T1.contents = 'Rocks'
[ "Find", "the", "number", "of", "different", "locations", "where", "Rocks", "are", "stored", "." ]
[ { "id": 1, "type": "table", "value": "warehouses" }, { "id": 5, "type": "column", "value": "warehouse" }, { "id": 2, "type": "column", "value": "contents" }, { "id": 4, "type": "column", "value": "location" }, { "id": 0, "type": "table", "value": "boxes" }, { "id": 3, "type": "value", "value": "Rocks" }, { "id": 6, "type": "column", "value": "code" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 7 ] }, { "entity_id": 4, "token_idxs": [ 5 ] }, { "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-COLUMN", "O", "B-VALUE", "O", "O", "O" ]
15,549
legislator
bird:train.json:4748
Current legislator Roger F. Wicker has not been a representative for how many terms?
SELECT SUM(CASE WHEN T1.official_full_name = 'Roger F. Wicker' THEN 1 ELSE 0 END) AS count FROM current AS T1 INNER JOIN `current-terms` AS T2 ON T1.bioguide_id = T2.bioguide WHERE T2.district IS NULL OR T2.district = ''
[ "Current", "legislator", "Roger", "F.", "Wicker", "has", "not", "been", "a", "representative", "for", "how", "many", "terms", "?" ]
[ { "id": 7, "type": "column", "value": "official_full_name" }, { "id": 8, "type": "value", "value": "Roger F. Wicker" }, { "id": 1, "type": "table", "value": "current-terms" }, { "id": 2, "type": "column", "value": "bioguide_id" }, { "id": 3, "type": "column", "value": "bioguide" }, { "id": 4, "type": "column", "value": "district" }, { "id": 0, "type": "table", "value": "current" }, { "id": 5, "type": "value", "value": "0" }, { "id": 6, "type": "value", "value": "1" } ]
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[ "B-TABLE", "O", "B-VALUE", "I-VALUE", "I-VALUE", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O" ]
15,550
race_track
spider:train_spider.json:752
What are the names and seatings for all tracks opened after 2000, ordered by seating?
SELECT name , seating FROM track WHERE year_opened > 2000 ORDER BY seating
[ "What", "are", "the", "names", "and", "seatings", "for", "all", "tracks", "opened", "after", "2000", ",", "ordered", "by", "seating", "?" ]
[ { "id": 3, "type": "column", "value": "year_opened" }, { "id": 2, "type": "column", "value": "seating" }, { "id": 0, "type": "table", "value": "track" }, { "id": 1, "type": "column", "value": "name" }, { "id": 4, "type": "value", "value": "2000" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "O", "O", "O", "B-TABLE", "B-COLUMN", "O", "B-VALUE", "O", "O", "O", "B-COLUMN", "O" ]
15,551
document_management
spider:train_spider.json:4523
Find the average access counts of documents with functional area "Acknowledgement".
SELECT avg(t1.access_count) FROM documents AS t1 JOIN document_functional_areas AS t2 ON t1.document_code = t2.document_code JOIN functional_areas AS t3 ON t2.functional_area_code = t3.functional_area_code WHERE t3.functional_area_description = "Acknowledgement"
[ "Find", "the", "average", "access", "counts", "of", "documents", "with", "functional", "area", "\"", "Acknowledgement", "\"", "." ]
[ { "id": 1, "type": "column", "value": "functional_area_description" }, { "id": 5, "type": "table", "value": "document_functional_areas" }, { "id": 6, "type": "column", "value": "functional_area_code" }, { "id": 0, "type": "table", "value": "functional_areas" }, { "id": 2, "type": "column", "value": "Acknowledgement" }, { "id": 7, "type": "column", "value": "document_code" }, { "id": 3, "type": "column", "value": "access_count" }, { "id": 4, "type": "table", "value": "documents" } ]
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[ "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "B-TABLE", "B-TABLE", "B-TABLE", "I-TABLE", "O", "B-COLUMN", "O", "O" ]
15,552
beer_factory
bird:train.json:5301
Which brewery brewed the most sold root beer in 2015?
SELECT T3.BreweryName FROM rootbeer AS T1 INNER JOIN `transaction` AS T2 ON T1.RootBeerID = T2.RootBeerID INNER JOIN rootbeerbrand AS T3 ON T1.BrandID = T3.BrandID WHERE T2.TransactionDate LIKE '2015%' GROUP BY T3.BrandID ORDER BY COUNT(T1.BrandID) DESC LIMIT 1
[ "Which", "brewery", "brewed", "the", "most", "sold", "root", "beer", "in", "2015", "?" ]
[ { "id": 3, "type": "column", "value": "transactiondate" }, { "id": 2, "type": "table", "value": "rootbeerbrand" }, { "id": 1, "type": "column", "value": "breweryname" }, { "id": 6, "type": "table", "value": "transaction" }, { "id": 7, "type": "column", "value": "rootbeerid" }, { "id": 5, "type": "table", "value": "rootbeer" }, { "id": 0, "type": "column", "value": "brandid" }, { "id": 4, "type": "value", "value": "2015%" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 1 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [ 9 ] }, { "entity_id": 5, "token_idxs": [ 6, 7 ] }, { "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", "B-TABLE", "I-TABLE", "O", "B-VALUE", "O" ]
15,553
car_road_race
bird:test.json:1345
What is the most common type of engine?
SELECT Engine FROM driver GROUP BY Engine ORDER BY COUNT(*) DESC LIMIT 1
[ "What", "is", "the", "most", "common", "type", "of", "engine", "?" ]
[ { "id": 0, "type": "table", "value": "driver" }, { "id": 1, "type": "column", "value": "engine" } ]
[ { "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" ]
15,554
retail_world
bird:train.json:6588
How many companies are there in the city of London?
SELECT COUNT(CompanyName) FROM Customers WHERE City = 'London'
[ "How", "many", "companies", "are", "there", "in", "the", "city", "of", "London", "?" ]
[ { "id": 3, "type": "column", "value": "companyname" }, { "id": 0, "type": "table", "value": "customers" }, { "id": 2, "type": "value", "value": "London" }, { "id": 1, "type": "column", "value": "city" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 7 ] }, { "entity_id": 2, "token_idxs": [ 9 ] }, { "entity_id": 3, "token_idxs": [ 2 ] }, { "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", "O", "O", "B-COLUMN", "O", "B-VALUE", "O" ]
15,555
soccer_2016
bird:train.json:1810
State the name of captain keeper of the match no.419117.
SELECT T3.Player_Name FROM Player_Match AS T1 INNER JOIN Rolee AS T2 ON T1.Role_Id = T2.Role_Id INNER JOIN Player AS T3 ON T1.Player_Id = T3.Player_Id WHERE T1.Match_Id = '419117' AND T2.Role_Desc = 'CaptainKeeper'
[ "State", "the", "name", "of", "captain", "keeper", "of", "the", "match", "no.419117", "." ]
[ { "id": 8, "type": "value", "value": "CaptainKeeper" }, { "id": 2, "type": "table", "value": "player_match" }, { "id": 0, "type": "column", "value": "player_name" }, { "id": 4, "type": "column", "value": "player_id" }, { "id": 7, "type": "column", "value": "role_desc" }, { "id": 5, "type": "column", "value": "match_id" }, { "id": 9, "type": "column", "value": "role_id" }, { "id": 1, "type": "table", "value": "player" }, { "id": 6, "type": "value", "value": "419117" }, { "id": 3, "type": "table", "value": "rolee" } ]
[ { "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": [] }, { "entity_id": 5, "token_idxs": [ 8 ] }, { "entity_id": 6, "token_idxs": [ 9 ] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [ 4, 5 ] }, { "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-VALUE", "I-VALUE", "O", "O", "B-COLUMN", "B-VALUE", "O" ]
15,556
cars
bird:train.json:3141
List the name of the most expensive car.
SELECT T1.car_name FROM data AS T1 INNER JOIN price AS T2 ON T1.ID = T2.ID ORDER BY T2.price DESC LIMIT 1
[ "List", "the", "name", "of", "the", "most", "expensive", "car", "." ]
[ { "id": 0, "type": "column", "value": "car_name" }, { "id": 2, "type": "table", "value": "price" }, { "id": 3, "type": "column", "value": "price" }, { "id": 1, "type": "table", "value": "data" }, { "id": 4, "type": "column", "value": "id" } ]
[ { "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-COLUMN", "O", "O", "O", "O", "O", "O" ]
15,557
law_episode
bird:train.json:1324
On what episode did Julia Roberts win the "Outstanding Guest Actress in a Drama Series" award during the 1999 Primetime Emmy Awards? Tell me her role.
SELECT T3.episode_id, T2.role FROM Person AS T1 INNER JOIN Award AS T2 ON T1.person_id = T2.person_id INNER JOIN Episode AS T3 ON T2.episode_id = T3.episode_id WHERE T2.year = 1999 AND T2.award = 'Outstanding Guest Actress in a Drama Series' AND T2.organization = 'Primetime Emmy Awards' AND T1.name = 'Julia Roberts' AND T2.result = 'Nominee'
[ "On", "what", "episode", "did", "Julia", "Roberts", "win", "the", "\"", "Outstanding", "Guest", "Actress", "in", "a", "Drama", "Series", "\"", "award", "during", "the", "1999", "Primetime", "Emmy", "Awards", "?", "Tell", "me", "her", "role", "." ]
[ { "id": 8, "type": "value", "value": "Outstanding Guest Actress in a Drama Series" }, { "id": 10, "type": "value", "value": "Primetime Emmy Awards" }, { "id": 12, "type": "value", "value": "Julia Roberts" }, { "id": 9, "type": "column", "value": "organization" }, { "id": 0, "type": "column", "value": "episode_id" }, { "id": 15, "type": "column", "value": "person_id" }, { "id": 2, "type": "table", "value": "episode" }, { "id": 14, "type": "value", "value": "Nominee" }, { "id": 3, "type": "table", "value": "person" }, { "id": 13, "type": "column", "value": "result" }, { "id": 4, "type": "table", "value": "award" }, { "id": 7, "type": "column", "value": "award" }, { "id": 1, "type": "column", "value": "role" }, { "id": 5, "type": "column", "value": "year" }, { "id": 6, "type": "value", "value": "1999" }, { "id": 11, "type": "column", "value": "name" } ]
[ { "entity_id": 0, "token_idxs": [ 3 ] }, { "entity_id": 1, "token_idxs": [ 28 ] }, { "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": [ 20 ] }, { "entity_id": 7, "token_idxs": [ 17 ] }, { "entity_id": 8, "token_idxs": [ 9, 10, 11, 12, 13, 14, 15 ] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [ 21, 22, 23 ] }, { "entity_id": 11, "token_idxs": [ 26 ] }, { "entity_id": 12, "token_idxs": [ 4, 5 ] }, { "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", "B-VALUE", "I-VALUE", "O", "O", "O", "B-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "O", "B-COLUMN", "O", "O", "B-VALUE", "B-VALUE", "I-VALUE", "I-VALUE", "O", "O", "B-COLUMN", "O", "B-COLUMN", "O" ]
15,558
simpson_episodes
bird:train.json:4312
How many people who were born after 1970 are animation executive producer?
SELECT COUNT(*) FROM Person AS T1 INNER JOIN Credit AS T2 ON T1.name = T2.person WHERE STRFTIME(T1.birthdate) > '1970' AND T2.role = 'animation executive producer';
[ "How", "many", "people", "who", "were", "born", "after", "1970", "are", "animation", "executive", "producer", "?" ]
[ { "id": 6, "type": "value", "value": "animation executive producer" }, { "id": 7, "type": "column", "value": "birthdate" }, { "id": 0, "type": "table", "value": "person" }, { "id": 1, "type": "table", "value": "credit" }, { "id": 3, "type": "column", "value": "person" }, { "id": 2, "type": "column", "value": "name" }, { "id": 4, "type": "value", "value": "1970" }, { "id": 5, "type": "column", "value": "role" } ]
[ { "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": [ 2 ] }, { "entity_id": 6, "token_idxs": [ 9, 10, 11 ] }, { "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", "B-VALUE", "O", "B-VALUE", "I-VALUE", "I-VALUE", "O" ]
15,559
image_and_language
bird:train.json:7494
Give the bounding box of the kite in image no.2324765.
SELECT T2.X, T2.Y, T2.W, T2.H FROM OBJ_CLASSES AS T1 INNER JOIN IMG_OBJ AS T2 ON T1.OBJ_CLASS_ID = T2.OBJ_CLASS_ID WHERE T2.IMG_ID = 2324765 AND T1.OBJ_CLASS = 'kite'
[ "Give", "the", "bounding", "box", "of", "the", "kite", "in", "image", "no.2324765", "." ]
[ { "id": 6, "type": "column", "value": "obj_class_id" }, { "id": 4, "type": "table", "value": "obj_classes" }, { "id": 9, "type": "column", "value": "obj_class" }, { "id": 5, "type": "table", "value": "img_obj" }, { "id": 8, "type": "value", "value": "2324765" }, { "id": 7, "type": "column", "value": "img_id" }, { "id": 10, "type": "value", "value": "kite" }, { "id": 0, "type": "column", "value": "x" }, { "id": 1, "type": "column", "value": "y" }, { "id": 2, "type": "column", "value": "w" }, { "id": 3, "type": "column", "value": "h" } ]
[ { "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": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [ 9 ] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [ 6 ] }, { "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-VALUE", "O", "O", "B-VALUE", "O" ]
15,560
chicago_crime
bird:train.json:8691
Among the incidents reported in Harrison, what percentage are disorderly conduct?
SELECT COUNT(CASE WHEN T3.title = 'Disorderly Conduct' THEN T2.report_no END) * 100.0 / COUNT(T2.report_no) AS per FROM District AS T1 INNER JOIN Crime AS T2 ON T2.district_no = T1.district_no INNER JOIN FBI_Code AS T3 ON T3.fbi_code_no = T2.fbi_code_no WHERE T1.district_name = 'Harrison'
[ "Among", "the", "incidents", "reported", "in", "Harrison", ",", "what", "percentage", "are", "disorderly", "conduct", "?" ]
[ { "id": 10, "type": "value", "value": "Disorderly Conduct" }, { "id": 1, "type": "column", "value": "district_name" }, { "id": 5, "type": "column", "value": "fbi_code_no" }, { "id": 8, "type": "column", "value": "district_no" }, { "id": 7, "type": "column", "value": "report_no" }, { "id": 0, "type": "table", "value": "fbi_code" }, { "id": 2, "type": "value", "value": "Harrison" }, { "id": 3, "type": "table", "value": "district" }, { "id": 4, "type": "table", "value": "crime" }, { "id": 6, "type": "value", "value": "100.0" }, { "id": 9, "type": "column", "value": "title" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 5 ] }, { "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": [ 3 ] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [ 10, 11 ] }, { "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", "B-VALUE", "O", "O", "O", "O", "B-VALUE", "I-VALUE", "O" ]
15,561
cre_Doc_Workflow
bird:test.json:2023
Who is the author of the document "Travel to Brazil"?
SELECT author_name FROM Documents WHERE document_name = "Travel to Brazil"
[ "Who", "is", "the", "author", "of", "the", "document", "\"", "Travel", "to", "Brazil", "\"", "?" ]
[ { "id": 3, "type": "column", "value": "Travel to Brazil" }, { "id": 2, "type": "column", "value": "document_name" }, { "id": 1, "type": "column", "value": "author_name" }, { "id": 0, "type": "table", "value": "documents" } ]
[ { "entity_id": 0, "token_idxs": [ 6 ] }, { "entity_id": 1, "token_idxs": [ 3 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 8, 9, 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", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "O", "O" ]
15,562
financial
bird:dev.json:152
What is the average number of crimes committed in 1995 in regions where the number exceeds 4000 and the region has accounts that are opened starting from the year 1997?
SELECT AVG(T1.A15) FROM district AS T1 INNER JOIN account AS T2 ON T1.district_id = T2.district_id WHERE STRFTIME('%Y', T2.date) >= '1997' AND T1.A15 > 4000
[ "What", "is", "the", "average", "number", "of", "crimes", "committed", "in", "1995", "in", "regions", "where", "the", "number", "exceeds", "4000", "and", "the", "region", "has", "accounts", "that", "are", "opened", "starting", "from", "the", "year", "1997", "?" ]
[ { "id": 3, "type": "column", "value": "district_id" }, { "id": 0, "type": "table", "value": "district" }, { "id": 1, "type": "table", "value": "account" }, { "id": 4, "type": "value", "value": "1997" }, { "id": 5, "type": "value", "value": "4000" }, { "id": 7, "type": "column", "value": "date" }, { "id": 2, "type": "column", "value": "a15" }, { "id": 6, "type": "value", "value": "%Y" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 21 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [ 29 ] }, { "entity_id": 5, "token_idxs": [ 16 ] }, { "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", "O", "O", "O", "O", "B-VALUE", "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "O", "B-VALUE", "O" ]
15,563
customer_deliveries
spider:train_spider.json:2845
How many different payment methods are there?
SELECT count(DISTINCT payment_method) FROM customers
[ "How", "many", "different", "payment", "methods", "are", "there", "?" ]
[ { "id": 1, "type": "column", "value": "payment_method" }, { "id": 0, "type": "table", "value": "customers" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 3, 4 ] }, { "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", "B-COLUMN", "I-COLUMN", "O", "O", "O" ]
15,565
pilot_1
bird:test.json:1103
Find the names of all pilots who have a plane named Piper Cub and is under 35.
SELECT pilot_name FROM PilotSkills WHERE age < 35 AND plane_name = 'Piper Cub'
[ "Find", "the", "names", "of", "all", "pilots", "who", "have", "a", "plane", "named", "Piper", "Cub", "and", "is", "under", "35", "." ]
[ { "id": 0, "type": "table", "value": "pilotskills" }, { "id": 1, "type": "column", "value": "pilot_name" }, { "id": 4, "type": "column", "value": "plane_name" }, { "id": 5, "type": "value", "value": "Piper Cub" }, { "id": 2, "type": "column", "value": "age" }, { "id": 3, "type": "value", "value": "35" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 5 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 16 ] }, { "entity_id": 4, "token_idxs": [ 9, 10 ] }, { "entity_id": 5, "token_idxs": [ 11, 12 ] }, { "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-COLUMN", "O", "O", "O", "B-COLUMN", "I-COLUMN", "B-VALUE", "I-VALUE", "O", "O", "O", "B-VALUE", "O" ]
15,566
retail_world
bird:train.json:6313
Please list the home phone numbers of the employees who are in charge of the sales in the territories in the Eastern Region.
SELECT T1.HomePhone FROM Employees AS T1 INNER JOIN EmployeeTerritories AS T2 ON T1.EmployeeID = T2.EmployeeID INNER JOIN Territories AS T3 ON T2.TerritoryID = T3.TerritoryID INNER JOIN Region AS T4 ON T3.RegionID = T4.RegionID WHERE T4.RegionDescription = 'Eastern ' GROUP BY T1.HomePhone
[ "Please", "list", "the", "home", "phone", "numbers", "of", "the", "employees", "who", "are", "in", "charge", "of", "the", "sales", "in", "the", "territories", "in", "the", "Eastern", "Region", "." ]
[ { "id": 7, "type": "table", "value": "employeeterritories" }, { "id": 2, "type": "column", "value": "regiondescription" }, { "id": 4, "type": "table", "value": "territories" }, { "id": 8, "type": "column", "value": "territoryid" }, { "id": 9, "type": "column", "value": "employeeid" }, { "id": 0, "type": "column", "value": "homephone" }, { "id": 6, "type": "table", "value": "employees" }, { "id": 3, "type": "value", "value": "Eastern " }, { "id": 5, "type": "column", "value": "regionid" }, { "id": 1, "type": "table", "value": "region" } ]
[ { "entity_id": 0, "token_idxs": [ 3, 4 ] }, { "entity_id": 1, "token_idxs": [ 22 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 21 ] }, { "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": [ 18 ] }, { "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", "B-TABLE", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "B-VALUE", "B-TABLE", "O" ]
15,567
train_station
spider:train_spider.json:6613
show the train name and station name for each train.
SELECT T2.name , T3.name FROM train_station AS T1 JOIN station AS T2 ON T1.station_id = T2.station_id JOIN train AS T3 ON T3.train_id = T1.train_id
[ "show", "the", "train", "name", "and", "station", "name", "for", "each", "train", "." ]
[ { "id": 2, "type": "table", "value": "train_station" }, { "id": 5, "type": "column", "value": "station_id" }, { "id": 4, "type": "column", "value": "train_id" }, { "id": 3, "type": "table", "value": "station" }, { "id": 1, "type": "table", "value": "train" }, { "id": 0, "type": "column", "value": "name" } ]
[ { "entity_id": 0, "token_idxs": [ 6 ] }, { "entity_id": 1, "token_idxs": [ 9 ] }, { "entity_id": 2, "token_idxs": [ 4 ] }, { "entity_id": 3, "token_idxs": [ 5 ] }, { "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", "B-TABLE", "B-TABLE", "B-COLUMN", "O", "O", "B-TABLE", "O" ]
15,568
sakila_1
spider:train_spider.json:3002
Where does the staff member with the first name Elsa live?
SELECT T2.address FROM staff AS T1 JOIN address AS T2 ON T1.address_id = T2.address_id WHERE T1.first_name = 'Elsa'
[ "Where", "does", "the", "staff", "member", "with", "the", "first", "name", "Elsa", "live", "?" ]
[ { "id": 3, "type": "column", "value": "first_name" }, { "id": 5, "type": "column", "value": "address_id" }, { "id": 0, "type": "column", "value": "address" }, { "id": 2, "type": "table", "value": "address" }, { "id": 1, "type": "table", "value": "staff" }, { "id": 4, "type": "value", "value": "Elsa" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 3 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 7, 8 ] }, { "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", "B-TABLE", "O", "O", "O", "B-COLUMN", "I-COLUMN", "B-VALUE", "O", "O" ]
15,569
synthea
bird:train.json:1542
List down the full name of Irish patients diagnosed with the prevalent diseases that have an occurrence greater than the 96% of the average occurrences of all conditions.
SELECT DISTINCT T2.first, T2.last FROM conditions AS T1 INNER JOIN patients AS T2 ON T1.PATIENT = T2.patient INNER JOIN all_prevalences AS T3 ON T1.DESCRIPTION = T3.ITEM WHERE T2.ethnicity = 'irish' AND 100 * CAST(T3.OCCURRENCES AS REAL) / ( SELECT AVG(OCCURRENCES) FROM all_prevalences ) > 96
[ "List", "down", "the", "full", "name", "of", "Irish", "patients", "diagnosed", "with", "the", "prevalent", "diseases", "that", "have", "an", "occurrence", "greater", "than", "the", "96", "%", "of", "the", "average", "occurrences", "of", "all", "conditions", "." ]
[ { "id": 2, "type": "table", "value": "all_prevalences" }, { "id": 5, "type": "column", "value": "description" }, { "id": 12, "type": "column", "value": "occurrences" }, { "id": 3, "type": "table", "value": "conditions" }, { "id": 7, "type": "column", "value": "ethnicity" }, { "id": 4, "type": "table", "value": "patients" }, { "id": 10, "type": "column", "value": "patient" }, { "id": 0, "type": "column", "value": "first" }, { "id": 8, "type": "value", "value": "irish" }, { "id": 1, "type": "column", "value": "last" }, { "id": 6, "type": "column", "value": "item" }, { "id": 11, "type": "value", "value": "100" }, { "id": 9, "type": "value", "value": "96" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 0 ] }, { "entity_id": 2, "token_idxs": [ 11 ] }, { "entity_id": 3, "token_idxs": [ 28 ] }, { "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": [ 6 ] }, { "entity_id": 9, "token_idxs": [ 20 ] }, { "entity_id": 10, "token_idxs": [ 7 ] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [ 25 ] }, { "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-VALUE", "B-COLUMN", "O", "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "O", "O", "B-VALUE", "O", "O", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O" ]
15,570
allergy_1
spider:train_spider.json:531
Find the different first names and cities of the students who have allergy to milk or cat.
SELECT DISTINCT T1.fname , T1.city_code FROM Student AS T1 JOIN Has_Allergy AS T2 ON T1.stuid = T2.stuid WHERE T2.Allergy = "Milk" OR T2.Allergy = "Cat"
[ "Find", "the", "different", "first", "names", "and", "cities", "of", "the", "students", "who", "have", "allergy", "to", "milk", "or", "cat", "." ]
[ { "id": 3, "type": "table", "value": "has_allergy" }, { "id": 1, "type": "column", "value": "city_code" }, { "id": 2, "type": "table", "value": "student" }, { "id": 5, "type": "column", "value": "allergy" }, { "id": 0, "type": "column", "value": "fname" }, { "id": 4, "type": "column", "value": "stuid" }, { "id": 6, "type": "column", "value": "Milk" }, { "id": 7, "type": "column", "value": "Cat" } ]
[ { "entity_id": 0, "token_idxs": [ 4 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 11 ] }, { "entity_id": 4, "token_idxs": [ 9 ] }, { "entity_id": 5, "token_idxs": [ 12 ] }, { "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", "O", "B-COLUMN", "O", "O", "O", "O", "B-COLUMN", "O", "B-TABLE", "B-COLUMN", "O", "B-COLUMN", "O", "B-COLUMN", "O" ]
15,571
store_1
spider:train_spider.json:554
What are the titles of all the albums alphabetically ascending?
SELECT title FROM albums ORDER BY title;
[ "What", "are", "the", "titles", "of", "all", "the", "albums", "alphabetically", "ascending", "?" ]
[ { "id": 0, "type": "table", "value": "albums" }, { "id": 1, "type": "column", "value": "title" } ]
[ { "entity_id": 0, "token_idxs": [ 7 ] }, { "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": [] }, { "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", "B-TABLE", "O", "O", "O" ]
15,572
works_cycles
bird:train.json:7056
Among the employees who have a pay rate of above 40, how many of them are male?
SELECT SUM(CASE WHEN T2.Gender = 'M' THEN 1 ELSE 0 END) FROM EmployeePayHistory AS T1 INNER JOIN Employee AS T2 ON T1.BusinessEntityID = T2.BusinessEntityID WHERE T1.Rate > 40
[ "Among", "the", "employees", "who", "have", "a", "pay", "rate", "of", "above", "40", ",", "how", "many", "of", "them", "are", "male", "?" ]
[ { "id": 0, "type": "table", "value": "employeepayhistory" }, { "id": 4, "type": "column", "value": "businessentityid" }, { "id": 1, "type": "table", "value": "employee" }, { "id": 7, "type": "column", "value": "gender" }, { "id": 2, "type": "column", "value": "rate" }, { "id": 3, "type": "value", "value": "40" }, { "id": 5, "type": "value", "value": "0" }, { "id": 6, "type": "value", "value": "1" }, { "id": 8, "type": "value", "value": "M" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 2 ] }, { "entity_id": 2, "token_idxs": [ 7 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [ 10 ] }, { "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", "O", "O", "B-VALUE", "O", "O", "O", "O", "O", "O", "O", "O" ]
15,573
app_store
bird:train.json:2539
What are the content ratings for the apps that have "gr8" in their comments?
SELECT DISTINCT T1.`Content Rating` FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T2.Translated_Review LIKE '%gr8%'
[ "What", "are", "the", "content", "ratings", "for", "the", "apps", "that", "have", "\"", "gr8", "\"", "in", "their", "comments", "?" ]
[ { "id": 3, "type": "column", "value": "translated_review" }, { "id": 0, "type": "column", "value": "Content Rating" }, { "id": 2, "type": "table", "value": "user_reviews" }, { "id": 1, "type": "table", "value": "playstore" }, { "id": 4, "type": "value", "value": "%gr8%" }, { "id": 5, "type": "column", "value": "app" } ]
[ { "entity_id": 0, "token_idxs": [ 3, 4 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [ 11 ] }, { "entity_id": 5, "token_idxs": [ 7 ] }, { "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", "B-COLUMN", "O", "O", "O", "B-VALUE", "O", "O", "O", "O", "O" ]
15,574
flight_company
spider:train_spider.json:6375
What is the id, name and IATA code of the airport that had most number of flights?
SELECT T1.id , T1.name , T1.IATA FROM airport AS T1 JOIN flight AS T2 ON T1.id = T2.airport_id GROUP BY T2.id ORDER BY count(*) DESC LIMIT 1
[ "What", "is", "the", "i", "d", ",", "name", "and", "IATA", "code", "of", "the", "airport", "that", "had", "most", "number", "of", "flights", "?" ]
[ { "id": 5, "type": "column", "value": "airport_id" }, { "id": 3, "type": "table", "value": "airport" }, { "id": 4, "type": "table", "value": "flight" }, { "id": 1, "type": "column", "value": "name" }, { "id": 2, "type": "column", "value": "iata" }, { "id": 0, "type": "column", "value": "id" } ]
[ { "entity_id": 0, "token_idxs": [ 3, 4 ] }, { "entity_id": 1, "token_idxs": [ 6 ] }, { "entity_id": 2, "token_idxs": [ 8 ] }, { "entity_id": 3, "token_idxs": [ 12 ] }, { "entity_id": 4, "token_idxs": [ 18 ] }, { "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", "B-COLUMN", "O", "B-COLUMN", "O", "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "B-TABLE", "O" ]
15,575
network_2
spider:train_spider.json:4406
How many type of jobs do they have?
SELECT count(DISTINCT job) FROM Person
[ "How", "many", "type", "of", "jobs", "do", "they", "have", "?" ]
[ { "id": 0, "type": "table", "value": "person" }, { "id": 1, "type": "column", "value": "job" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 4 ] }, { "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", "B-COLUMN", "O", "O", "O", "O" ]
15,576
retails
bird:train.json:6731
How many customers are there in India?
SELECT COUNT(T1.c_custkey) FROM customer AS T1 INNER JOIN nation AS T2 ON T1.c_nationkey = T2.n_nationkey WHERE T2.n_name = 'INDIA'
[ "How", "many", "customers", "are", "there", "in", "India", "?" ]
[ { "id": 5, "type": "column", "value": "c_nationkey" }, { "id": 6, "type": "column", "value": "n_nationkey" }, { "id": 4, "type": "column", "value": "c_custkey" }, { "id": 0, "type": "table", "value": "customer" }, { "id": 1, "type": "table", "value": "nation" }, { "id": 2, "type": "column", "value": "n_name" }, { "id": 3, "type": "value", "value": "INDIA" } ]
[ { "entity_id": 0, "token_idxs": [ 2 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 6 ] }, { "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", "B-VALUE", "O" ]
15,577
legislator
bird:train.json:4846
Does Thomas Carnes have an account on ballotpedia.org?
SELECT CASE WHEN ballotpedia_id IS NULL THEN 'doesn''t have' ELSE 'have' END AS HaveorNot FROM historical WHERE first_name = 'Thomas' AND last_name = 'Carnes'
[ "Does", "Thomas", "Carnes", "have", "an", "account", "on", "ballotpedia.org", "?" ]
[ { "id": 7, "type": "column", "value": "ballotpedia_id" }, { "id": 6, "type": "value", "value": "doesn't have" }, { "id": 0, "type": "table", "value": "historical" }, { "id": 2, "type": "column", "value": "first_name" }, { "id": 4, "type": "column", "value": "last_name" }, { "id": 3, "type": "value", "value": "Thomas" }, { "id": 5, "type": "value", "value": "Carnes" }, { "id": 1, "type": "value", "value": "have" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 3 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 1 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [ 2 ] }, { "entity_id": 6, "token_idxs": [ 0 ] }, { "entity_id": 7, "token_idxs": [ 7 ] }, { "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-VALUE", "B-VALUE", "B-VALUE", "B-VALUE", "O", "O", "O", "B-COLUMN", "O" ]
15,578
retails
bird:train.json:6801
How many of the line items that have a quantity greater than 40 have been shipped by air?
SELECT COUNT(l_linenumber) FROM lineitem WHERE l_quantity > 40 AND l_shipmode = 'AIR'
[ "How", "many", "of", "the", "line", "items", "that", "have", "a", "quantity", "greater", "than", "40", "have", "been", "shipped", "by", "air", "?" ]
[ { "id": 1, "type": "column", "value": "l_linenumber" }, { "id": 2, "type": "column", "value": "l_quantity" }, { "id": 4, "type": "column", "value": "l_shipmode" }, { "id": 0, "type": "table", "value": "lineitem" }, { "id": 5, "type": "value", "value": "AIR" }, { "id": 3, "type": "value", "value": "40" } ]
[ { "entity_id": 0, "token_idxs": [ 4, 5 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 9 ] }, { "entity_id": 3, "token_idxs": [ 12 ] }, { "entity_id": 4, "token_idxs": [ 15 ] }, { "entity_id": 5, "token_idxs": [ 17 ] }, { "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-TABLE", "I-TABLE", "O", "O", "O", "B-COLUMN", "O", "O", "B-VALUE", "O", "O", "B-COLUMN", "O", "B-VALUE", "O" ]
15,579
simpson_episodes
bird:train.json:4330
Which episode has the highest number of vote of the star score?
SELECT episode_id FROM Vote ORDER BY votes DESC LIMIT 1;
[ "Which", "episode", "has", "the", "highest", "number", "of", "vote", "of", "the", "star", "score", "?" ]
[ { "id": 1, "type": "column", "value": "episode_id" }, { "id": 2, "type": "column", "value": "votes" }, { "id": 0, "type": "table", "value": "vote" } ]
[ { "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", "O", "O", "O", "O" ]
15,580
cookbook
bird:train.json:8924
Which recipe has the highest calories?
SELECT T1.title FROM Recipe AS T1 INNER JOIN Nutrition AS T2 ON T1.recipe_id = T2.recipe_id ORDER BY T2.calories DESC LIMIT 1
[ "Which", "recipe", "has", "the", "highest", "calories", "?" ]
[ { "id": 2, "type": "table", "value": "nutrition" }, { "id": 4, "type": "column", "value": "recipe_id" }, { "id": 3, "type": "column", "value": "calories" }, { "id": 1, "type": "table", "value": "recipe" }, { "id": 0, "type": "column", "value": "title" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 1 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 5 ] }, { "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", "O", "O", "O", "B-COLUMN", "O" ]
15,581
beer_factory
bird:train.json:5334
Give me the full name of the first customer, and tell me how long ago he or she wrote his or her first review since making his or her first purchase.
SELECT T1.First, T1.Last , strftime('%J', ReviewDate) - strftime('%J', FirstPurchaseDate) AS TIMEAGO FROM customers AS T1 INNER JOIN rootbeerreview AS T2 ON T1.CustomerID = T2.CustomerID LIMIT 1
[ "Give", "me", "the", "full", "name", "of", "the", "first", "customer", ",", "and", "tell", "me", "how", "long", "ago", "he", "or", "she", "wrote", "his", "or", "her", "first", "review", "since", "making", "his", "or", "her", "first", "purchase", "." ]
[ { "id": 7, "type": "column", "value": "firstpurchasedate" }, { "id": 3, "type": "table", "value": "rootbeerreview" }, { "id": 4, "type": "column", "value": "customerid" }, { "id": 6, "type": "column", "value": "reviewdate" }, { "id": 2, "type": "table", "value": "customers" }, { "id": 0, "type": "column", "value": "first" }, { "id": 1, "type": "column", "value": "last" }, { "id": 5, "type": "value", "value": "%J" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "B-TABLE", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "B-COLUMN", "O", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O" ]
15,582
movie_3
bird:train.json:9348
List down all film IDs of comedy film titles.
SELECT T1.film_id FROM film AS T1 INNER JOIN film_category AS T2 ON T1.film_id = T2.film_id INNER JOIN category AS T3 ON T3.category_id = T2.category_id WHERE T3.name = 'comedy'
[ "List", "down", "all", "film", "IDs", "of", "comedy", "film", "titles", "." ]
[ { "id": 5, "type": "table", "value": "film_category" }, { "id": 6, "type": "column", "value": "category_id" }, { "id": 1, "type": "table", "value": "category" }, { "id": 0, "type": "column", "value": "film_id" }, { "id": 3, "type": "value", "value": "comedy" }, { "id": 2, "type": "column", "value": "name" }, { "id": 4, "type": "table", "value": "film" } ]
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[ "O", "O", "O", "B-TABLE", "B-COLUMN", "O", "B-VALUE", "O", "O", "O" ]
15,583
disney
bird:train.json:4729
How many voice-actors were involved in the Bambi movie?
SELECT COUNT(DISTINCT 'voice-actor') FROM `voice-actors` WHERE movie = 'Bambi'
[ "How", "many", "voice", "-", "actors", "were", "involved", "in", "the", "Bambi", "movie", "?" ]
[ { "id": 0, "type": "table", "value": "voice-actors" }, { "id": 3, "type": "value", "value": "voice-actor" }, { "id": 1, "type": "column", "value": "movie" }, { "id": 2, "type": "value", "value": "Bambi" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 10 ] }, { "entity_id": 2, "token_idxs": [ 9 ] }, { "entity_id": 3, "token_idxs": [ 2, 3, 4 ] }, { "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-VALUE", "I-VALUE", "I-VALUE", "O", "O", "O", "O", "B-VALUE", "B-COLUMN", "O" ]
15,584
bike_1
spider:train_spider.json:212
What is the average latitude and longitude of all starting stations for the trips?
SELECT avg(T1.lat) , avg(T1.long) FROM station AS T1 JOIN trip AS T2 ON T1.id = T2.start_station_id
[ "What", "is", "the", "average", "latitude", "and", "longitude", "of", "all", "starting", "stations", "for", "the", "trips", "?" ]
[ { "id": 5, "type": "column", "value": "start_station_id" }, { "id": 0, "type": "table", "value": "station" }, { "id": 1, "type": "table", "value": "trip" }, { "id": 3, "type": "column", "value": "long" }, { "id": 2, "type": "column", "value": "lat" }, { "id": 4, "type": "column", "value": "id" } ]
[ { "entity_id": 0, "token_idxs": [ 10 ] }, { "entity_id": 1, "token_idxs": [ 13 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 6 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [ 9 ] }, { "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", "B-TABLE", "O", "O", "B-TABLE", "O" ]
15,585
book_1
bird:test.json:578
Give the titles of books authored by Plato that have a sale price lower than the average sale price across all books.
SELECT T1.title FROM Book AS T1 JOIN Author_book AS T2 ON T1.isbn = T2.isbn JOIN Author AS T3 ON T2.Author = T3.idAuthor WHERE T3.name = "Plato" AND T1.saleprice < (SELECT avg(saleprice) FROM Book)
[ "Give", "the", "titles", "of", "books", "authored", "by", "Plato", "that", "have", "a", "sale", "price", "lower", "than", "the", "average", "sale", "price", "across", "all", "books", "." ]
[ { "id": 3, "type": "table", "value": "author_book" }, { "id": 8, "type": "column", "value": "saleprice" }, { "id": 5, "type": "column", "value": "idauthor" }, { "id": 1, "type": "table", "value": "author" }, { "id": 4, "type": "column", "value": "author" }, { "id": 0, "type": "column", "value": "title" }, { "id": 7, "type": "column", "value": "Plato" }, { "id": 2, "type": "table", "value": "book" }, { "id": 6, "type": "column", "value": "name" }, { "id": 9, "type": "column", "value": "isbn" } ]
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[ "O", "O", "B-COLUMN", "O", "B-TABLE", "B-COLUMN", "B-TABLE", "B-COLUMN", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "O", "O" ]
15,586
talkingdata
bird:train.json:1062
What are the labels' IDs of online shopping and online malls categories?
SELECT label_id FROM label_categories WHERE category IN ('online shopping', 'online malls')
[ "What", "are", "the", "labels", "'", "IDs", "of", "online", "shopping", "and", "online", "malls", "categories", "?" ]
[ { "id": 0, "type": "table", "value": "label_categories" }, { "id": 3, "type": "value", "value": "online shopping" }, { "id": 4, "type": "value", "value": "online malls" }, { "id": 1, "type": "column", "value": "label_id" }, { "id": 2, "type": "column", "value": "category" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 3, 4, 5 ] }, { "entity_id": 2, "token_idxs": [ 12 ] }, { "entity_id": 3, "token_idxs": [ 7, 8 ] }, { "entity_id": 4, "token_idxs": [ 10, 11 ] }, { "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", "I-COLUMN", "O", "B-VALUE", "I-VALUE", "O", "B-VALUE", "I-VALUE", "B-COLUMN", "O" ]
15,587
talkingdata
bird:train.json:1167
How many app users belong to label ID of "48"?
SELECT COUNT(app_id) FROM app_labels WHERE label_id = 48
[ "How", "many", "app", "users", "belong", "to", "label", "ID", "of", "\"", "48", "\"", "?" ]
[ { "id": 0, "type": "table", "value": "app_labels" }, { "id": 1, "type": "column", "value": "label_id" }, { "id": 3, "type": "column", "value": "app_id" }, { "id": 2, "type": "value", "value": "48" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 6, 7 ] }, { "entity_id": 2, "token_idxs": [ 10 ] }, { "entity_id": 3, "token_idxs": [ 2 ] }, { "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", "O", "B-COLUMN", "I-COLUMN", "O", "O", "B-VALUE", "O", "O" ]
15,588
hockey
bird:train.json:7651
Please list the years in which the NHL League had shots recorded while the goalie was on the ice.
SELECT DISTINCT year FROM Goalies WHERE lgID = 'NHL' AND SA IS NOT NULL
[ "Please", "list", "the", "years", "in", "which", "the", "NHL", "League", "had", "shots", "recorded", "while", "the", "goalie", "was", "on", "the", "ice", "." ]
[ { "id": 0, "type": "table", "value": "goalies" }, { "id": 1, "type": "column", "value": "year" }, { "id": 2, "type": "column", "value": "lgid" }, { "id": 3, "type": "value", "value": "NHL" }, { "id": 4, "type": "column", "value": "sa" } ]
[ { "entity_id": 0, "token_idxs": [ 14 ] }, { "entity_id": 1, "token_idxs": [ 3 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 7 ] }, { "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", "B-VALUE", "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "O", "O" ]
15,589
bike_1
spider:train_spider.json:168
On which day has it neither been foggy nor rained in the zip code of 94107?
SELECT date FROM weather WHERE zip_code = 94107 AND EVENTS != "Fog" AND EVENTS != "Rain"
[ "On", "which", "day", "has", "it", "neither", "been", "foggy", "nor", "rained", "in", "the", "zip", "code", "of", "94107", "?" ]
[ { "id": 2, "type": "column", "value": "zip_code" }, { "id": 0, "type": "table", "value": "weather" }, { "id": 4, "type": "column", "value": "events" }, { "id": 3, "type": "value", "value": "94107" }, { "id": 1, "type": "column", "value": "date" }, { "id": 6, "type": "column", "value": "Rain" }, { "id": 5, "type": "column", "value": "Fog" } ]
[ { "entity_id": 0, "token_idxs": [ 5 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 12, 13 ] }, { "entity_id": 3, "token_idxs": [ 15 ] }, { "entity_id": 4, "token_idxs": [ 6 ] }, { "entity_id": 5, "token_idxs": [ 7 ] }, { "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", "O", "O", "O", "B-TABLE", "B-COLUMN", "B-COLUMN", "O", "B-COLUMN", "O", "O", "B-COLUMN", "I-COLUMN", "O", "B-VALUE", "O" ]
15,590
student_loan
bird:train.json:4491
Define the gender of "student995" and his/her enrolled schools.
SELECT IIF(T2.name IS NULL, 'female', 'male') AS gen , T1.school FROM enrolled AS T1 LEFT JOIN male AS T2 ON T2.name = T1.name WHERE T1.name = 'student995'
[ "Define", "the", "gender", "of", "\"", "student995", "\"", "and", "his", "/", "her", "enrolled", "schools", "." ]
[ { "id": 4, "type": "value", "value": "student995" }, { "id": 1, "type": "table", "value": "enrolled" }, { "id": 0, "type": "column", "value": "school" }, { "id": 5, "type": "value", "value": "female" }, { "id": 2, "type": "table", "value": "male" }, { "id": 3, "type": "column", "value": "name" }, { "id": 6, "type": "value", "value": "male" } ]
[ { "entity_id": 0, "token_idxs": [ 12 ] }, { "entity_id": 1, "token_idxs": [ 11 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [ 5 ] }, { "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-VALUE", "O", "O", "O", "O", "O", "B-TABLE", "B-COLUMN", "O" ]
15,591
mondial_geo
bird:train.json:8335
How many percent of the mountains on Andes which are non-volcanic?
SELECT CAST(SUM(CASE WHEN type != 'volcano' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(*) FROM mountain WHERE Mountains = 'Andes'
[ "How", "many", "percent", "of", "the", "mountains", "on", "Andes", "which", "are", "non", "-", "volcanic", "?" ]
[ { "id": 1, "type": "column", "value": "mountains" }, { "id": 0, "type": "table", "value": "mountain" }, { "id": 7, "type": "value", "value": "volcano" }, { "id": 2, "type": "value", "value": "Andes" }, { "id": 6, "type": "column", "value": "type" }, { "id": 3, "type": "value", "value": "100" }, { "id": 4, "type": "value", "value": "0" }, { "id": 5, "type": "value", "value": "1" } ]
[ { "entity_id": 0, "token_idxs": [ 5 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 7 ] }, { "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": [ 12 ] }, { "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", "B-VALUE", "O", "O", "O", "O", "B-VALUE", "O" ]
15,592
menu
bird:train.json:5486
What is the average page number of the menus that have the dish "Clear green turtle"?
SELECT AVG(T1.page_number) FROM MenuPage AS T1 INNER JOIN MenuItem AS T2 ON T1.id = T2.menu_page_id INNER JOIN Dish AS T3 ON T2.dish_id = T3.id WHERE T3.name = 'Clear green turtle'
[ "What", "is", "the", "average", "page", "number", "of", "the", "menus", "that", "have", "the", "dish", "\"", "Clear", "green", "turtle", "\"", "?" ]
[ { "id": 2, "type": "value", "value": "Clear green turtle" }, { "id": 8, "type": "column", "value": "menu_page_id" }, { "id": 3, "type": "column", "value": "page_number" }, { "id": 4, "type": "table", "value": "menupage" }, { "id": 5, "type": "table", "value": "menuitem" }, { "id": 6, "type": "column", "value": "dish_id" }, { "id": 0, "type": "table", "value": "dish" }, { "id": 1, "type": "column", "value": "name" }, { "id": 7, "type": "column", "value": "id" } ]
[ { "entity_id": 0, "token_idxs": [ 12 ] }, { "entity_id": 1, "token_idxs": [ 5 ] }, { "entity_id": 2, "token_idxs": [ 14, 15, 16 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [ 4 ] }, { "entity_id": 5, "token_idxs": [ 8 ] }, { "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-TABLE", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "O", "B-TABLE", "O", "B-VALUE", "I-VALUE", "I-VALUE", "O", "O" ]
15,593
art_1
bird:test.json:1204
Find the names of all sculptures located in gallery 226.
SELECT title FROM sculptures WHERE LOCATION = "Gallery 226"
[ "Find", "the", "names", "of", "all", "sculptures", "located", "in", "gallery", "226", "." ]
[ { "id": 3, "type": "column", "value": "Gallery 226" }, { "id": 0, "type": "table", "value": "sculptures" }, { "id": 2, "type": "column", "value": "location" }, { "id": 1, "type": "column", "value": "title" } ]
[ { "entity_id": 0, "token_idxs": [ 5 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 6, 7 ] }, { "entity_id": 3, "token_idxs": [ 8, 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", "B-TABLE", "B-COLUMN", "I-COLUMN", "B-COLUMN", "I-COLUMN", "O" ]
15,594
customers_and_addresses
spider:train_spider.json:6139
Find the names of customers who never placed an order.
SELECT customer_name FROM customers EXCEPT SELECT t1.customer_name FROM customers AS t1 JOIN customer_orders AS t2 ON t1.customer_id = t2.customer_id
[ "Find", "the", "names", "of", "customers", "who", "never", "placed", "an", "order", "." ]
[ { "id": 2, "type": "table", "value": "customer_orders" }, { "id": 1, "type": "column", "value": "customer_name" }, { "id": 3, "type": "column", "value": "customer_id" }, { "id": 0, "type": "table", "value": "customers" } ]
[ { "entity_id": 0, "token_idxs": [ 4 ] }, { "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", "B-TABLE", "O", "O", "O", "O", "O", "O" ]
15,595
genes
bird:train.json:2502
Lists all genes by identifier number located in the cytoplasm and whose function is metabolism.
SELECT DISTINCT GeneID FROM Genes WHERE Localization = 'cytoplasm' AND Function = 'METABOLISM'
[ "Lists", "all", "genes", "by", "identifier", "number", "located", "in", "the", "cytoplasm", "and", "whose", "function", "is", "metabolism", "." ]
[ { "id": 2, "type": "column", "value": "localization" }, { "id": 5, "type": "value", "value": "METABOLISM" }, { "id": 3, "type": "value", "value": "cytoplasm" }, { "id": 4, "type": "column", "value": "function" }, { "id": 1, "type": "column", "value": "geneid" }, { "id": 0, "type": "table", "value": "genes" } ]
[ { "entity_id": 0, "token_idxs": [ 2 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 6, 7 ] }, { "entity_id": 3, "token_idxs": [ 9 ] }, { "entity_id": 4, "token_idxs": [ 12 ] }, { "entity_id": 5, "token_idxs": [ 14 ] }, { "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", "B-COLUMN", "I-COLUMN", "O", "B-VALUE", "O", "O", "B-COLUMN", "O", "B-VALUE", "O" ]
15,596
simpson_episodes
bird:train.json:4360
What is the title of episode nominated for WGA Award (TV) with votes greater than 1000?
SELECT DISTINCT T2.title FROM Award AS T1 INNER JOIN Episode AS T2 ON T1.episode_id = T2.episode_id WHERE T2.votes > 1000 AND T1.award_category = 'WGA Award (TV)' AND T1.result = 'Nominee';
[ "What", "is", "the", "title", "of", "episode", "nominated", "for", "WGA", "Award", "(", "TV", ")", "with", "votes", "greater", "than", "1000", "?" ]
[ { "id": 6, "type": "column", "value": "award_category" }, { "id": 7, "type": "value", "value": "WGA Award (TV)" }, { "id": 3, "type": "column", "value": "episode_id" }, { "id": 2, "type": "table", "value": "episode" }, { "id": 9, "type": "value", "value": "Nominee" }, { "id": 8, "type": "column", "value": "result" }, { "id": 0, "type": "column", "value": "title" }, { "id": 1, "type": "table", "value": "award" }, { "id": 4, "type": "column", "value": "votes" }, { "id": 5, "type": "value", "value": "1000" } ]
[ { "entity_id": 0, "token_idxs": [ 3 ] }, { "entity_id": 1, "token_idxs": [ 9 ] }, { "entity_id": 2, "token_idxs": [ 5 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [ 14 ] }, { "entity_id": 5, "token_idxs": [ 17 ] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [ 8, 10, 11, 12 ] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [ 6 ] }, { "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", "B-TABLE", "B-VALUE", "O", "B-VALUE", "B-TABLE", "B-VALUE", "I-VALUE", "I-VALUE", "O", "B-COLUMN", "O", "O", "B-VALUE", "O" ]
15,597
simpson_episodes
bird:train.json:4171
How many episodes aired in the year 2009 have over 15% of voters giving 10 stars in star score?
SELECT COUNT(*) FROM Episode AS T1 INNER JOIN Vote AS T2 ON T2.episode_id = T1.episode_id WHERE SUBSTR(T1.air_date, 1, 4) = '2009' AND T2.stars = 10 AND T2.percent > 15;
[ "How", "many", "episodes", "aired", "in", "the", "year", "2009", "have", "over", "15", "%", "of", "voters", "giving", "10", "stars", "in", "star", "score", "?" ]
[ { "id": 2, "type": "column", "value": "episode_id" }, { "id": 8, "type": "column", "value": "air_date" }, { "id": 0, "type": "table", "value": "episode" }, { "id": 6, "type": "column", "value": "percent" }, { "id": 4, "type": "column", "value": "stars" }, { "id": 1, "type": "table", "value": "vote" }, { "id": 3, "type": "value", "value": "2009" }, { "id": 5, "type": "value", "value": "10" }, { "id": 7, "type": "value", "value": "15" }, { "id": 9, "type": "value", "value": "1" }, { "id": 10, "type": "value", "value": "4" } ]
[ { "entity_id": 0, "token_idxs": [ 2 ] }, { "entity_id": 1, "token_idxs": [ 13 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 7 ] }, { "entity_id": 4, "token_idxs": [ 16 ] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [ 10 ] }, { "entity_id": 8, "token_idxs": [ 3 ] }, { "entity_id": 9, "token_idxs": [ 15 ] }, { "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", "O", "O", "B-VALUE", "O", "O", "B-VALUE", "O", "O", "B-TABLE", "O", "B-VALUE", "B-COLUMN", "O", "O", "O", "O" ]
15,598
planet_1
bird:test.json:1923
Which planets that have exact one employee has clearance? List planets' name.
SELECT T3.Name FROM Has_Clearance AS T1 JOIN Employee AS T2 ON T1.Employee = T2.EmployeeID JOIN Planet AS T3 ON T1.Planet = T3.PlanetID GROUP BY T1.Planet HAVING count(*) = 1;
[ "Which", "planets", "that", "have", "exact", "one", "employee", "has", "clearance", "?", "List", "planets", "'", "name", "." ]
[ { "id": 4, "type": "table", "value": "has_clearance" }, { "id": 8, "type": "column", "value": "employeeid" }, { "id": 5, "type": "table", "value": "employee" }, { "id": 6, "type": "column", "value": "planetid" }, { "id": 7, "type": "column", "value": "employee" }, { "id": 0, "type": "column", "value": "planet" }, { "id": 2, "type": "table", "value": "planet" }, { "id": 1, "type": "column", "value": "name" }, { "id": 3, "type": "value", "value": "1" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 13 ] }, { "entity_id": 2, "token_idxs": [ 1 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [ 7, 8 ] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [ 6 ] }, { "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", "O", "O", "O", "O", "B-COLUMN", "B-TABLE", "I-TABLE", "O", "O", "O", "O", "B-COLUMN", "O" ]
15,599
workshop_paper
spider:train_spider.json:5817
Find the author for each submission and list them in ascending order of submission score.
SELECT Author FROM submission ORDER BY Scores ASC
[ "Find", "the", "author", "for", "each", "submission", "and", "list", "them", "in", "ascending", "order", "of", "submission", "score", "." ]
[ { "id": 0, "type": "table", "value": "submission" }, { "id": 1, "type": "column", "value": "author" }, { "id": 2, "type": "column", "value": "scores" } ]
[ { "entity_id": 0, "token_idxs": [ 5 ] }, { "entity_id": 1, "token_idxs": [ 2 ] }, { "entity_id": 2, "token_idxs": [ 14 ] }, { "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", "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O" ]
15,600
sakila_1
spider:train_spider.json:2961
What is the full name and id of the customer who has the lowest total amount of payment?
SELECT T1.first_name , T1.last_name , T1.customer_id FROM customer AS T1 JOIN payment AS T2 ON T1.customer_id = T2.customer_id GROUP BY T1.customer_id ORDER BY sum(amount) ASC LIMIT 1
[ "What", "is", "the", "full", "name", "and", "i", "d", "of", "the", "customer", "who", "has", "the", "lowest", "total", "amount", "of", "payment", "?" ]
[ { "id": 0, "type": "column", "value": "customer_id" }, { "id": 1, "type": "column", "value": "first_name" }, { "id": 2, "type": "column", "value": "last_name" }, { "id": 3, "type": "table", "value": "customer" }, { "id": 4, "type": "table", "value": "payment" }, { "id": 5, "type": "column", "value": "amount" } ]
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[ "O", "O", "O", "O", "B-COLUMN", "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "B-COLUMN", "O", "B-TABLE", "O" ]
15,601
music_1
spider:train_spider.json:3550
How many songs, on average, are sung by a female artist?
SELECT avg(T2.rating) FROM artist AS T1 JOIN song AS T2 ON T1.artist_name = T2.artist_name WHERE T1.gender = "Female"
[ "How", "many", "songs", ",", "on", "average", ",", "are", "sung", "by", "a", "female", "artist", "?" ]
[ { "id": 5, "type": "column", "value": "artist_name" }, { "id": 0, "type": "table", "value": "artist" }, { "id": 2, "type": "column", "value": "gender" }, { "id": 3, "type": "column", "value": "Female" }, { "id": 4, "type": "column", "value": "rating" }, { "id": 1, "type": "table", "value": "song" } ]
[ { "entity_id": 0, "token_idxs": [ 12 ] }, { "entity_id": 1, "token_idxs": [ 2 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 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", "B-TABLE", "O", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "B-TABLE", "O" ]
15,602
works_cycles
bird:train.json:7186
What is the job position currently occupied by Ken J Sánchez?
SELECT T1.JobTitle FROM Employee AS T1 INNER JOIN Person AS T2 ON T1.BusinessEntityID = T2.BusinessEntityID WHERE T2.FirstName = 'Ken' AND T2.MiddleName = 'J' AND T2.LastName = 'Sánchez'
[ "What", "is", "the", "job", "position", "currently", "occupied", "by", "Ken", "J", "Sánchez", "?" ]
[ { "id": 3, "type": "column", "value": "businessentityid" }, { "id": 6, "type": "column", "value": "middlename" }, { "id": 4, "type": "column", "value": "firstname" }, { "id": 0, "type": "column", "value": "jobtitle" }, { "id": 1, "type": "table", "value": "employee" }, { "id": 8, "type": "column", "value": "lastname" }, { "id": 9, "type": "value", "value": "Sánchez" }, { "id": 2, "type": "table", "value": "person" }, { "id": 5, "type": "value", "value": "Ken" }, { "id": 7, "type": "value", "value": "J" } ]
[ { "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": [] }, { "entity_id": 5, "token_idxs": [ 8 ] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [ 9 ] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [ 10 ] }, { "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-VALUE", "B-VALUE", "B-VALUE", "O" ]
15,603
trains
bird:train.json:698
Please list the IDs of all the cars with double sides on trains that run in the west direction.
SELECT T1.id FROM cars AS T1 INNER JOIN trains AS T2 ON T1.train_id = T2.id WHERE T2.direction = 'east' AND T1.sides = 'double'
[ "Please", "list", "the", "IDs", "of", "all", "the", "cars", "with", "double", "sides", "on", "trains", "that", "run", "in", "the", "west", "direction", "." ]
[ { "id": 4, "type": "column", "value": "direction" }, { "id": 3, "type": "column", "value": "train_id" }, { "id": 2, "type": "table", "value": "trains" }, { "id": 7, "type": "value", "value": "double" }, { "id": 6, "type": "column", "value": "sides" }, { "id": 1, "type": "table", "value": "cars" }, { "id": 5, "type": "value", "value": "east" }, { "id": 0, "type": "column", "value": "id" } ]
[ { "entity_id": 0, "token_idxs": [ 3 ] }, { "entity_id": 1, "token_idxs": [ 7 ] }, { "entity_id": 2, "token_idxs": [ 12 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [ 18 ] }, { "entity_id": 5, "token_idxs": [ 17 ] }, { "entity_id": 6, "token_idxs": [ 10 ] }, { "entity_id": 7, "token_idxs": [ 9 ] }, { "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-TABLE", "O", "B-VALUE", "B-COLUMN", "O", "B-TABLE", "O", "O", "O", "O", "B-VALUE", "B-COLUMN", "O" ]
15,604
activity_1
spider:train_spider.json:6787
Show the ids of the students who don't participate in any activity.
SELECT StuID FROM Student EXCEPT SELECT StuID FROM Participates_in
[ "Show", "the", "ids", "of", "the", "students", "who", "do", "n't", "participate", "in", "any", "activity", "." ]
[ { "id": 1, "type": "table", "value": "participates_in" }, { "id": 0, "type": "table", "value": "student" }, { "id": 2, "type": "column", "value": "stuid" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 9, 10 ] }, { "entity_id": 2, "token_idxs": [ 5 ] }, { "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-COLUMN", "O", "O", "O", "B-TABLE", "I-TABLE", "O", "O", "O" ]
15,605
warehouse_1
bird:test.json:1690
What are the average and total values across all boxes?
SELECT avg(value) , sum(value) FROM boxes
[ "What", "are", "the", "average", "and", "total", "values", "across", "all", "boxes", "?" ]
[ { "id": 0, "type": "table", "value": "boxes" }, { "id": 1, "type": "column", "value": "value" } ]
[ { "entity_id": 0, "token_idxs": [ 9 ] }, { "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-COLUMN", "O", "O", "B-TABLE", "O" ]
15,606
college_1
spider:train_spider.json:3229
How many courses are offered?
SELECT count(DISTINCT crs_code) FROM CLASS
[ "How", "many", "courses", "are", "offered", "?" ]
[ { "id": 1, "type": "column", "value": "crs_code" }, { "id": 0, "type": "table", "value": "class" } ]
[ { "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": [] }, { "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" ]
15,607
food_inspection
bird:train.json:8787
Which restaurant has the highest total number of high risk violations?
SELECT T2.name FROM violations AS T1 INNER JOIN businesses AS T2 ON T1.business_id = T2.business_id WHERE T1.risk_category = 'High Risk' GROUP BY T2.name ORDER BY COUNT(T2.name) DESC LIMIT 1
[ "Which", "restaurant", "has", "the", "highest", "total", "number", "of", "high", "risk", "violations", "?" ]
[ { "id": 3, "type": "column", "value": "risk_category" }, { "id": 5, "type": "column", "value": "business_id" }, { "id": 1, "type": "table", "value": "violations" }, { "id": 2, "type": "table", "value": "businesses" }, { "id": 4, "type": "value", "value": "High Risk" }, { "id": 0, "type": "column", "value": "name" } ]
[ { "entity_id": 0, "token_idxs": [ 6 ] }, { "entity_id": 1, "token_idxs": [ 10 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "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", "O", "O", "O", "O", "O", "B-COLUMN", "O", "B-VALUE", "I-VALUE", "B-TABLE", "O" ]
15,608
hr_1
spider:train_spider.json:3435
display all the details from Employees table for those employees who was hired before 2002-06-21.
SELECT * FROM employees WHERE hire_date < '2002-06-21'
[ "display", "all", "the", "details", "from", "Employees", "table", "for", "those", "employees", "who", "was", "hired", "before", "2002", "-", "06", "-", "21", "." ]
[ { "id": 2, "type": "value", "value": "2002-06-21" }, { "id": 0, "type": "table", "value": "employees" }, { "id": 1, "type": "column", "value": "hire_date" } ]
[ { "entity_id": 0, "token_idxs": [ 9 ] }, { "entity_id": 1, "token_idxs": [ 12 ] }, { "entity_id": 2, "token_idxs": [ 14, 15, 16, 17, 18 ] }, { "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", "B-TABLE", "O", "O", "B-COLUMN", "O", "B-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "O" ]
15,609
election_representative
spider:train_spider.json:1182
What are the names of representatives with more than 10000 votes in election?
SELECT T2.Name FROM election AS T1 JOIN representative AS T2 ON T1.Representative_ID = T2.Representative_ID WHERE Votes > 10000
[ "What", "are", "the", "names", "of", "representatives", "with", "more", "than", "10000", "votes", "in", "election", "?" ]
[ { "id": 5, "type": "column", "value": "representative_id" }, { "id": 2, "type": "table", "value": "representative" }, { "id": 1, "type": "table", "value": "election" }, { "id": 3, "type": "column", "value": "votes" }, { "id": 4, "type": "value", "value": "10000" }, { "id": 0, "type": "column", "value": "name" } ]
[ { "entity_id": 0, "token_idxs": [ 3 ] }, { "entity_id": 1, "token_idxs": [ 12 ] }, { "entity_id": 2, "token_idxs": [ 5 ] }, { "entity_id": 3, "token_idxs": [ 10 ] }, { "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", "B-COLUMN", "O", "B-TABLE", "O", "O", "O", "B-VALUE", "B-COLUMN", "O", "B-TABLE", "O" ]
15,610
inn_1
spider:train_spider.json:2622
List the name of all rooms sorted by their prices.
SELECT roomName FROM Rooms ORDER BY basePrice;
[ "List", "the", "name", "of", "all", "rooms", "sorted", "by", "their", "prices", "." ]
[ { "id": 2, "type": "column", "value": "baseprice" }, { "id": 1, "type": "column", "value": "roomname" }, { "id": 0, "type": "table", "value": "rooms" } ]
[ { "entity_id": 0, "token_idxs": [ 5 ] }, { "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", "O", "B-TABLE", "O", "O", "O", "B-COLUMN", "O" ]
15,611
professional_basketball
bird:train.json:2914
From 1980 to 1983, how many of the NBA All-Star players have more than 60% three point rate?
SELECT DISTINCT T2.playerID FROM player_allstar AS T1 INNER JOIN players_teams AS T2 ON T1.playerID = T2.playerID WHERE T2.year BETWEEN 1980 AND 1983 AND T1.three_made / T1.three_attempted > 0.6
[ "From", "1980", "to", "1983", ",", "how", "many", "of", "the", "NBA", "All", "-", "Star", "players", "have", "more", "than", "60", "%", "three", "point", "rate", "?" ]
[ { "id": 8, "type": "column", "value": "three_attempted" }, { "id": 1, "type": "table", "value": "player_allstar" }, { "id": 2, "type": "table", "value": "players_teams" }, { "id": 7, "type": "column", "value": "three_made" }, { "id": 0, "type": "column", "value": "playerid" }, { "id": 3, "type": "column", "value": "year" }, { "id": 4, "type": "value", "value": "1980" }, { "id": 5, "type": "value", "value": "1983" }, { "id": 6, "type": "value", "value": "0.6" } ]
[ { "entity_id": 0, "token_idxs": [ 13 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [ 1 ] }, { "entity_id": 5, "token_idxs": [ 3 ] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [ 19 ] }, { "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", "B-VALUE", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "O" ]
15,612
music_1
spider:train_spider.json:3590
What are the names of all male British artists?
SELECT artist_name FROM artist WHERE country = "UK" AND gender = "Male"
[ "What", "are", "the", "names", "of", "all", "male", "British", "artists", "?" ]
[ { "id": 1, "type": "column", "value": "artist_name" }, { "id": 2, "type": "column", "value": "country" }, { "id": 0, "type": "table", "value": "artist" }, { "id": 4, "type": "column", "value": "gender" }, { "id": 5, "type": "column", "value": "Male" }, { "id": 3, "type": "column", "value": "UK" } ]
[ { "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": [ 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": [] }, { "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-TABLE", "O" ]
15,613
european_football_2
bird:dev.json:1090
What is the long passing score of the oldest player?
SELECT t2.long_passing FROM Player AS t1 INNER JOIN Player_Attributes AS t2 ON t1.player_api_id = t2.player_api_id ORDER BY t1.birthday ASC LIMIT 1
[ "What", "is", "the", "long", "passing", "score", "of", "the", "oldest", "player", "?" ]
[ { "id": 2, "type": "table", "value": "player_attributes" }, { "id": 4, "type": "column", "value": "player_api_id" }, { "id": 0, "type": "column", "value": "long_passing" }, { "id": 3, "type": "column", "value": "birthday" }, { "id": 1, "type": "table", "value": "player" } ]
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[ "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "O", "O", "B-TABLE", "O" ]
15,614
shipping
bird:train.json:5644
How many trucks were manufactured in year 2009?
SELECT COUNT(truck_id) FROM truck WHERE model_year = 2009
[ "How", "many", "trucks", "were", "manufactured", "in", "year", "2009", "?" ]
[ { "id": 1, "type": "column", "value": "model_year" }, { "id": 3, "type": "column", "value": "truck_id" }, { "id": 0, "type": "table", "value": "truck" }, { "id": 2, "type": "value", "value": "2009" } ]
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[ "O", "O", "B-TABLE", "O", "O", "O", "O", "B-VALUE", "O" ]
15,615
video_games
bird:train.json:3440
Which publisher has published the most games in the 'Puzzle' genre?
SELECT T.publisher_name FROM ( SELECT T1.publisher_name, COUNT(T3.id) FROM publisher AS T1 INNER JOIN game_publisher AS T2 ON T1.id = T2.publisher_id INNER JOIN game AS T3 ON T2.game_id = T3.id INNER JOIN genre AS T4 ON T3.genre_id = T4.id WHERE T4.genre_name = 'Puzzle' GROUP BY T1.publisher_name ORDER BY COUNT(T3.id) DESC LIMIT 1 ) t
[ "Which", "publisher", "has", "published", "the", "most", "games", "in", "the", "'", "Puzzle", "'", "genre", "?" ]
[ { "id": 0, "type": "column", "value": "publisher_name" }, { "id": 8, "type": "table", "value": "game_publisher" }, { "id": 10, "type": "column", "value": "publisher_id" }, { "id": 2, "type": "column", "value": "genre_name" }, { "id": 7, "type": "table", "value": "publisher" }, { "id": 6, "type": "column", "value": "genre_id" }, { "id": 9, "type": "column", "value": "game_id" }, { "id": 3, "type": "value", "value": "Puzzle" }, { "id": 1, "type": "table", "value": "genre" }, { "id": 5, "type": "table", "value": "game" }, { "id": 4, "type": "column", "value": "id" } ]
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[ "O", "B-TABLE", "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "B-VALUE", "O", "B-TABLE", "O" ]
15,616
match_season
spider:train_spider.json:1081
Count the number of different official languages corresponding to countries that players who play Defender are from.
SELECT count(DISTINCT T1.Official_native_language) FROM country AS T1 JOIN match_season AS T2 ON T1.Country_id = T2.Country WHERE T2.Position = "Defender"
[ "Count", "the", "number", "of", "different", "official", "languages", "corresponding", "to", "countries", "that", "players", "who", "play", "Defender", "are", "from", "." ]
[ { "id": 4, "type": "column", "value": "official_native_language" }, { "id": 1, "type": "table", "value": "match_season" }, { "id": 5, "type": "column", "value": "country_id" }, { "id": 2, "type": "column", "value": "position" }, { "id": 3, "type": "column", "value": "Defender" }, { "id": 0, "type": "table", "value": "country" }, { "id": 6, "type": "column", "value": "country" } ]
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[ "B-COLUMN", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "B-COLUMN", "O", "O", "O", "O", "B-COLUMN", "O", "O", "O" ]
15,617
works_cycles
bird:train.json:7357
Please list the top 5 products with the most orders.
SELECT T1.Name FROM Product AS T1 INNER JOIN SalesOrderDetail AS T2 ON T1.ProductID = T2.ProductID GROUP BY T1.Name ORDER BY SUM(T2.OrderQty) DESC LIMIT 0, 5
[ "Please", "list", "the", "top", "5", "products", "with", "the", "most", "orders", "." ]
[ { "id": 2, "type": "table", "value": "salesorderdetail" }, { "id": 3, "type": "column", "value": "productid" }, { "id": 4, "type": "column", "value": "orderqty" }, { "id": 1, "type": "table", "value": "product" }, { "id": 0, "type": "column", "value": "name" } ]
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[ "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "B-COLUMN", "O" ]
15,618
world_development_indicators
bird:train.json:2143
Which indicator name uses the Weighted average method and has the lowest value?
SELECT T1.IndicatorName, MIN(T1.Value) FROM Indicators AS T1 INNER JOIN Series AS T2 ON T1.IndicatorName = T2.IndicatorName WHERE T2.AggregationMethod = 'Weighted average'
[ "Which", "indicator", "name", "uses", "the", "Weighted", "average", "method", "and", "has", "the", "lowest", "value", "?" ]
[ { "id": 3, "type": "column", "value": "aggregationmethod" }, { "id": 4, "type": "value", "value": "Weighted average" }, { "id": 0, "type": "column", "value": "indicatorname" }, { "id": 1, "type": "table", "value": "indicators" }, { "id": 2, "type": "table", "value": "series" }, { "id": 5, "type": "column", "value": "value" } ]
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[ "O", "B-TABLE", "B-COLUMN", "B-TABLE", "O", "B-VALUE", "I-VALUE", "B-COLUMN", "O", "O", "O", "O", "B-COLUMN", "O" ]
15,619
address
bird:train.json:5104
What is the difference in the most populated city of Allentown-Bethlehem-Easton, PA-NJ in 2020 against its population in 2010?
SELECT T1.population_2020 - T1.population_2010 AS result_data FROM zip_data AS T1 INNER JOIN CBSA AS T2 ON T1.CBSA = T2.CBSA WHERE T2.CBSA_name = 'Allentown-Bethlehem-Easton, PA-NJ' ORDER BY T1.population_2020 DESC LIMIT 1
[ "What", "is", "the", "difference", "in", "the", "most", "populated", "city", "of", "Allentown", "-", "Bethlehem", "-", "Easton", ",", "PA", "-", "NJ", "in", "2020", "against", "its", "population", "in", "2010", "?" ]
[ { "id": 3, "type": "value", "value": "Allentown-Bethlehem-Easton, PA-NJ" }, { "id": 4, "type": "column", "value": "population_2020" }, { "id": 5, "type": "column", "value": "population_2010" }, { "id": 2, "type": "column", "value": "cbsa_name" }, { "id": 0, "type": "table", "value": "zip_data" }, { "id": 1, "type": "table", "value": "cbsa" }, { "id": 6, "type": "column", "value": "cbsa" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "O" ]
15,620
olympics
bird:train.json:5073
In which city the 2004 Summer was held?
SELECT T2.city_name FROM games_city AS T1 INNER JOIN city AS T2 ON T1.city_id = T2.id INNER JOIN games AS T3 ON T1.games_id = T3.id WHERE T3.games_name = '2004 Summer'
[ "In", "which", "city", "the", "2004", "Summer", "was", "held", "?" ]
[ { "id": 3, "type": "value", "value": "2004 Summer" }, { "id": 2, "type": "column", "value": "games_name" }, { "id": 4, "type": "table", "value": "games_city" }, { "id": 0, "type": "column", "value": "city_name" }, { "id": 6, "type": "column", "value": "games_id" }, { "id": 8, "type": "column", "value": "city_id" }, { "id": 1, "type": "table", "value": "games" }, { "id": 5, "type": "table", "value": "city" }, { "id": 7, "type": "column", "value": "id" } ]
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[ "O", "O", "B-TABLE", "O", "B-VALUE", "I-VALUE", "O", "O", "O" ]
15,621
culture_company
spider:train_spider.json:6993
Return the average, maximum, and minimum budgets in millions for movies made before the year 2000.
SELECT avg(budget_million) , max(budget_million) , min(budget_million) FROM movie WHERE YEAR < 2000
[ "Return", "the", "average", ",", "maximum", ",", "and", "minimum", "budgets", "in", "millions", "for", "movies", "made", "before", "the", "year", "2000", "." ]
[ { "id": 3, "type": "column", "value": "budget_million" }, { "id": 0, "type": "table", "value": "movie" }, { "id": 1, "type": "column", "value": "year" }, { "id": 2, "type": "value", "value": "2000" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "O", "B-TABLE", "O", "O", "O", "B-COLUMN", "B-VALUE", "O" ]
15,623
device
spider:train_spider.json:5086
List the carriers of devices that have no devices in stock.
SELECT Carrier FROM device WHERE Device_ID NOT IN (SELECT Device_ID FROM stock)
[ "List", "the", "carriers", "of", "devices", "that", "have", "no", "devices", "in", "stock", "." ]
[ { "id": 2, "type": "column", "value": "device_id" }, { "id": 1, "type": "column", "value": "carrier" }, { "id": 0, "type": "table", "value": "device" }, { "id": 3, "type": "table", "value": "stock" } ]
[ { "entity_id": 0, "token_idxs": [ 8 ] }, { "entity_id": 1, "token_idxs": [ 2 ] }, { "entity_id": 2, "token_idxs": [ 4 ] }, { "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", "B-COLUMN", "O", "O", "O", "B-TABLE", "O", "B-TABLE", "O" ]
15,624
art_1
bird:test.json:1208
List the title and location of all sculptures.
SELECT title , LOCATION FROM sculptures
[ "List", "the", "title", "and", "location", "of", "all", "sculptures", "." ]
[ { "id": 0, "type": "table", "value": "sculptures" }, { "id": 2, "type": "column", "value": "location" }, { "id": 1, "type": "column", "value": "title" } ]
[ { "entity_id": 0, "token_idxs": [ 7 ] }, { "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", "O", "B-COLUMN", "O", "B-COLUMN", "O", "O", "B-TABLE", "O" ]
15,625
music_1
spider:train_spider.json:3565
Return the gender and name of artist who produced the song with the lowest resolution.
SELECT T1.gender , T1.artist_name FROM artist AS T1 JOIN song AS T2 ON T1.artist_name = T2.artist_name ORDER BY T2.resolution LIMIT 1
[ "Return", "the", "gender", "and", "name", "of", "artist", "who", "produced", "the", "song", "with", "the", "lowest", "resolution", "." ]
[ { "id": 1, "type": "column", "value": "artist_name" }, { "id": 4, "type": "column", "value": "resolution" }, { "id": 0, "type": "column", "value": "gender" }, { "id": 2, "type": "table", "value": "artist" }, { "id": 3, "type": "table", "value": "song" } ]
[ { "entity_id": 0, "token_idxs": [ 2 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 6 ] }, { "entity_id": 3, "token_idxs": [ 10 ] }, { "entity_id": 4, "token_idxs": [ 14 ] }, { "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-TABLE", "O", "O", "O", "B-TABLE", "O", "O", "O", "B-COLUMN", "O" ]
15,626
shakespeare
bird:train.json:2967
How many scenes are there on average in one act in Twelfth Night?
SELECT SUM(T2.Scene) / COUNT(T2.Act) FROM works AS T1 INNER JOIN chapters AS T2 ON T1.id = T2.work_id WHERE T1.Title = 'Twelfth Night'
[ "How", "many", "scenes", "are", "there", "on", "average", "in", "one", "act", "in", "Twelfth", "Night", "?" ]
[ { "id": 3, "type": "value", "value": "Twelfth Night" }, { "id": 1, "type": "table", "value": "chapters" }, { "id": 5, "type": "column", "value": "work_id" }, { "id": 0, "type": "table", "value": "works" }, { "id": 2, "type": "column", "value": "title" }, { "id": 6, "type": "column", "value": "scene" }, { "id": 7, "type": "column", "value": "act" }, { "id": 4, "type": "column", "value": "id" } ]
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[ "O", "O", "B-COLUMN", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "B-VALUE", "I-VALUE", "O" ]
15,628
books
bird:train.json:5988
How many percent of orders in 2020 used international shipping?
SELECT CAST(SUM(CASE WHEN T2.method_name = 'International' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(*) FROM cust_order AS T1 INNER JOIN shipping_method AS T2 ON T1.shipping_method_id = T2.method_id WHERE STRFTIME('%Y', T1.order_date) = '2020'
[ "How", "many", "percent", "of", "orders", "in", "2020", "used", "international", "shipping", "?" ]
[ { "id": 3, "type": "column", "value": "shipping_method_id" }, { "id": 1, "type": "table", "value": "shipping_method" }, { "id": 11, "type": "value", "value": "International" }, { "id": 10, "type": "column", "value": "method_name" }, { "id": 0, "type": "table", "value": "cust_order" }, { "id": 6, "type": "column", "value": "order_date" }, { "id": 4, "type": "column", "value": "method_id" }, { "id": 2, "type": "value", "value": "2020" }, { "id": 7, "type": "value", "value": "100" }, { "id": 5, "type": "value", "value": "%Y" }, { "id": 8, "type": "value", "value": "0" }, { "id": 9, "type": "value", "value": "1" } ]
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[ "O", "O", "O", "O", "B-COLUMN", "O", "B-VALUE", "O", "B-VALUE", "B-TABLE", "O" ]
15,629
customers_card_transactions
spider:train_spider.json:695
What is the phone and email for customer with first name Aniyah and last name Feest?
SELECT customer_phone , customer_email FROM Customers WHERE customer_first_name = "Aniyah" AND customer_last_name = "Feest"
[ "What", "is", "the", "phone", "and", "email", "for", "customer", "with", "first", "name", "Aniyah", "and", "last", "name", "Feest", "?" ]
[ { "id": 3, "type": "column", "value": "customer_first_name" }, { "id": 5, "type": "column", "value": "customer_last_name" }, { "id": 1, "type": "column", "value": "customer_phone" }, { "id": 2, "type": "column", "value": "customer_email" }, { "id": 0, "type": "table", "value": "customers" }, { "id": 4, "type": "column", "value": "Aniyah" }, { "id": 6, "type": "column", "value": "Feest" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "B-COLUMN", "I-COLUMN", "I-COLUMN", "B-COLUMN", "O", "O", "O", "B-COLUMN", "O" ]
15,630
trains
bird:train.json:728
What is the percentage of all the trains with at least 4 cars? List the directions of the said trains.
SELECT CAST(COUNT(CASE WHEN T2.trailPosi >= 4 THEN T1.id ELSE NULL END) AS REAL) * 100 / COUNT(T1.id) FROM trains AS T1 INNER JOIN ( SELECT train_id, MAX(position) AS trailPosi FROM cars GROUP BY train_id ) AS T2 ON T1.id = T2.train_id UNION ALL SELECT T1.direction FROM trains AS T1 INNER JOIN ( SELECT train_id, MAX(position) AS trailPosi FROM cars t GROUP BY train_id ) AS T2 ON T1.id = T2.train_id AND T2.trailPosi >= 4
[ "What", "is", "the", "percentage", "of", "all", "the", "trains", "with", "at", "least", "4", "cars", "?", "List", "the", "directions", "of", "the", "said", "trains", "." ]
[ { "id": 1, "type": "column", "value": "direction" }, { "id": 6, "type": "column", "value": "trailposi" }, { "id": 3, "type": "column", "value": "train_id" }, { "id": 8, "type": "column", "value": "position" }, { "id": 0, "type": "table", "value": "trains" }, { "id": 5, "type": "table", "value": "cars" }, { "id": 4, "type": "value", "value": "100" }, { "id": 2, "type": "column", "value": "id" }, { "id": 7, "type": "value", "value": "4" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-VALUE", "B-TABLE", "O", "O", "O", "B-COLUMN", "O", "O", "B-COLUMN", "B-TABLE", "O" ]
15,631
image_and_language
bird:train.json:7587
List all the ID of the images that have an attribute class of "horse".
SELECT T2.IMG_ID FROM ATT_CLASSES AS T1 INNER JOIN IMG_OBJ_ATT AS T2 ON T1.ATT_CLASS_ID = T2.ATT_CLASS_ID WHERE T1.ATT_CLASS = 'horse'
[ "List", "all", "the", "ID", "of", "the", "images", "that", "have", "an", "attribute", "class", "of", "\"", "horse", "\"", "." ]
[ { "id": 5, "type": "column", "value": "att_class_id" }, { "id": 1, "type": "table", "value": "att_classes" }, { "id": 2, "type": "table", "value": "img_obj_att" }, { "id": 3, "type": "column", "value": "att_class" }, { "id": 0, "type": "column", "value": "img_id" }, { "id": 4, "type": "value", "value": "horse" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "B-COLUMN", "O", "O", "B-VALUE", "O", "O" ]