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1,289
browser_web
spider:train_spider.json:1827
List the name of browsers in descending order by market share.
SELECT name FROM browser ORDER BY market_share DESC
[ "List", "the", "name", "of", "browsers", "in", "descending", "order", "by", "market", "share", "." ]
[ { "id": 2, "type": "column", "value": "market_share" }, { "id": 0, "type": "table", "value": "browser" }, { "id": 1, "type": "column", "value": "name" } ]
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[ "O", "O", "B-COLUMN", "O", "B-TABLE", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O" ]
1,292
soccer_2016
bird:train.json:1889
Count the total venues located in Pune City.
SELECT SUM(T1.Venue_Name) FROM Venue AS T1 INNER JOIN City AS T2 ON T1.City_Id = T2.City_Id WHERE T2.City_Name = 'Pune'
[ "Count", "the", "total", "venues", "located", "in", "Pune", "City", "." ]
[ { "id": 4, "type": "column", "value": "venue_name" }, { "id": 2, "type": "column", "value": "city_name" }, { "id": 5, "type": "column", "value": "city_id" }, { "id": 0, "type": "table", "value": "venue" }, { "id": 1, "type": "table", "value": "city" }, { "id": 3, "type": "value", "value": "Pune" } ]
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[ "O", "O", "O", "B-TABLE", "O", "O", "B-VALUE", "B-TABLE", "O" ]
1,293
products_gen_characteristics
spider:train_spider.json:5556
How many distinct characteristic names does the product "cumin" have?
SELECT count(DISTINCT t3.characteristic_name) FROM products AS t1 JOIN product_characteristics AS t2 ON t1.product_id = t2.product_id JOIN CHARACTERISTICS AS t3 ON t2.characteristic_id = t3.characteristic_id WHERE t1.product_name = "sesame"
[ "How", "many", "distinct", "characteristic", "names", "does", "the", "product", "\"", "cumin", "\"", "have", "?" ]
[ { "id": 5, "type": "table", "value": "product_characteristics" }, { "id": 3, "type": "column", "value": "characteristic_name" }, { "id": 6, "type": "column", "value": "characteristic_id" }, { "id": 0, "type": "table", "value": "characteristics" }, { "id": 1, "type": "column", "value": "product_name" }, { "id": 7, "type": "column", "value": "product_id" }, { "id": 4, "type": "table", "value": "products" }, { "id": 2, "type": "column", "value": "sesame" } ]
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[ "O", "O", "O", "B-TABLE", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "O", "O", "O" ]
1,294
soccer_1
spider:train_spider.json:1304
List all of the ids for left-footed players with a height between 180cm and 190cm.
SELECT player_api_id FROM Player WHERE height >= 180 AND height <= 190 INTERSECT SELECT player_api_id FROM Player_Attributes WHERE preferred_foot = "left"
[ "List", "all", "of", "the", "ids", "for", "left", "-", "footed", "players", "with", "a", "height", "between", "180", "cm", "and", "190", "cm", "." ]
[ { "id": 1, "type": "table", "value": "player_attributes" }, { "id": 3, "type": "column", "value": "preferred_foot" }, { "id": 2, "type": "column", "value": "player_api_id" }, { "id": 0, "type": "table", "value": "player" }, { "id": 5, "type": "column", "value": "height" }, { "id": 4, "type": "column", "value": "left" }, { "id": 6, "type": "value", "value": "180" }, { "id": 7, "type": "value", "value": "190" } ]
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[ "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "B-COLUMN", "O", "B-VALUE", "O", "O", "B-VALUE", "O", "O" ]
1,295
gymnast
spider:train_spider.json:1762
What is the most common hometown of gymnasts?
SELECT T2.Hometown FROM gymnast AS T1 JOIN people AS T2 ON T1.Gymnast_ID = T2.People_ID GROUP BY T2.Hometown ORDER BY COUNT(*) DESC LIMIT 1
[ "What", "is", "the", "most", "common", "hometown", "of", "gymnasts", "?" ]
[ { "id": 3, "type": "column", "value": "gymnast_id" }, { "id": 4, "type": "column", "value": "people_id" }, { "id": 0, "type": "column", "value": "hometown" }, { "id": 1, "type": "table", "value": "gymnast" }, { "id": 2, "type": "table", "value": "people" } ]
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[ "O", "O", "O", "O", "O", "B-COLUMN", "O", "B-TABLE", "O" ]
1,296
authors
bird:train.json:3594
What are the three journals that the papers written by Andrew Cain were published in? Please provide your answer with the full name of each journal.
SELECT T3.FullName FROM Paper AS T1 INNER JOIN PaperAuthor AS T2 ON T1.Id = T2.PaperId INNER JOIN Journal AS T3 ON T1.JournalId = T3.Id WHERE T2.Name = 'Andrew Cain'
[ "What", "are", "the", "three", "journals", "that", "the", "papers", "written", "by", "Andrew", "Cain", "were", "published", "in", "?", "Please", "provide", "your", "answer", "with", "the", "full", "name", "of", "each", "journal", "." ]
[ { "id": 3, "type": "value", "value": "Andrew Cain" }, { "id": 5, "type": "table", "value": "paperauthor" }, { "id": 6, "type": "column", "value": "journalid" }, { "id": 0, "type": "column", "value": "fullname" }, { "id": 1, "type": "table", "value": "journal" }, { "id": 8, "type": "column", "value": "paperid" }, { "id": 4, "type": "table", "value": "paper" }, { "id": 2, "type": "column", "value": "name" }, { "id": 7, "type": "column", "value": "id" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "B-VALUE", "I-VALUE", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "B-COLUMN", "O", "O", "B-TABLE", "O" ]
1,297
shakespeare
bird:train.json:2996
Which of Shakespeare's work has chapter description of "A field near Windsor"?
SELECT T2.Title FROM chapters AS T1 INNER JOIN works AS T2 ON T1.work_id = T2.id WHERE T1.Description = 'A field near Windsor.'
[ "Which", "of", "Shakespeare", "'s", "work", "has", "chapter", "description", "of", "\"", "A", "field", "near", "Windsor", "\"", "?" ]
[ { "id": 4, "type": "value", "value": "A field near Windsor." }, { "id": 3, "type": "column", "value": "description" }, { "id": 1, "type": "table", "value": "chapters" }, { "id": 5, "type": "column", "value": "work_id" }, { "id": 0, "type": "column", "value": "title" }, { "id": 2, "type": "table", "value": "works" }, { "id": 6, "type": "column", "value": "id" } ]
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[ "O", "O", "O", "O", "B-TABLE", "O", "B-TABLE", "B-COLUMN", "O", "O", "B-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "O", "O" ]
1,298
student_loan
bird:train.json:4501
How many students enlist in the air force organization?
SELECT COUNT(name) FROM enlist WHERE organ = 'air_force'
[ "How", "many", "students", "enlist", "in", "the", "air", "force", "organization", "?" ]
[ { "id": 2, "type": "value", "value": "air_force" }, { "id": 0, "type": "table", "value": "enlist" }, { "id": 1, "type": "column", "value": "organ" }, { "id": 3, "type": "column", "value": "name" } ]
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[ "O", "O", "O", "B-TABLE", "O", "O", "B-VALUE", "I-VALUE", "B-COLUMN", "O" ]
1,299
soccer_2
spider:train_spider.json:5024
Find the names of either colleges in LA with greater than 15000 size or in state AZ with less than 13000 enrollment.
SELECT cName FROM College WHERE enr < 13000 AND state = "AZ" UNION SELECT cName FROM College WHERE enr > 15000 AND state = "LA"
[ "Find", "the", "names", "of", "either", "colleges", "in", "LA", "with", "greater", "than", "15000", "size", "or", "in", "state", "AZ", "with", "less", "than", "13000", "enrollment", "." ]
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1,300
car_racing
bird:test.json:1612
What is the list of drivers ordered by points in descending order?
SELECT Driver FROM driver ORDER BY Points DESC
[ "What", "is", "the", "list", "of", "drivers", "ordered", "by", "points", "in", "descending", "order", "?" ]
[ { "id": 0, "type": "table", "value": "driver" }, { "id": 1, "type": "column", "value": "driver" }, { "id": 2, "type": "column", "value": "points" } ]
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[ "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "B-COLUMN", "O", "O", "O", "O" ]
1,301
small_bank_1
spider:train_spider.json:1799
What is the name corresponding to the accoung with the lowest sum of checking and savings balances?
SELECT T1.name FROM accounts AS T1 JOIN checking AS T2 ON T1.custid = T2.custid JOIN savings AS T3 ON T1.custid = T3.custid ORDER BY T2.balance + T3.balance LIMIT 1
[ "What", "is", "the", "name", "corresponding", "to", "the", "accoung", "with", "the", "lowest", "sum", "of", "checking", "and", "savings", "balances", "?" ]
[ { "id": 2, "type": "table", "value": "accounts" }, { "id": 3, "type": "table", "value": "checking" }, { "id": 1, "type": "table", "value": "savings" }, { "id": 5, "type": "column", "value": "balance" }, { "id": 4, "type": "column", "value": "custid" }, { "id": 0, "type": "column", "value": "name" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "B-TABLE", "O", "B-TABLE", "B-COLUMN", "O" ]
1,302
donor
bird:train.json:3193
Please list the types of resources that the vendor Lakeshore Learning Materials has provided for the projects.
SELECT DISTINCT project_resource_type FROM resources WHERE vendor_name = 'Lakeshore Learning Materials'
[ "Please", "list", "the", "types", "of", "resources", "that", "the", "vendor", "Lakeshore", "Learning", "Materials", "has", "provided", "for", "the", "projects", "." ]
[ { "id": 3, "type": "value", "value": "Lakeshore Learning Materials" }, { "id": 1, "type": "column", "value": "project_resource_type" }, { "id": 2, "type": "column", "value": "vendor_name" }, { "id": 0, "type": "table", "value": "resources" } ]
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[ "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "B-COLUMN", "B-VALUE", "I-VALUE", "I-VALUE", "O", "O", "O", "O", "O", "O" ]
1,303
food_inspection_2
bird:train.json:6132
Who is responsible for most of the inspections? Give the full name.
SELECT T.first_name, T.last_name FROM ( SELECT T2.employee_id, T2.first_name, T2.last_name, COUNT(T1.inspection_id) FROM inspection AS T1 INNER JOIN employee AS T2 ON T1.employee_id = T2.employee_id GROUP BY T2.employee_id, T2.first_name, T2.last_name ORDER BY COUNT(T1.inspection_id) DESC LIMIT 1 ) AS T
[ "Who", "is", "responsible", "for", "most", "of", "the", "inspections", "?", "Give", "the", "full", "name", "." ]
[ { "id": 5, "type": "column", "value": "inspection_id" }, { "id": 2, "type": "column", "value": "employee_id" }, { "id": 0, "type": "column", "value": "first_name" }, { "id": 3, "type": "table", "value": "inspection" }, { "id": 1, "type": "column", "value": "last_name" }, { "id": 4, "type": "table", "value": "employee" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 12 ] }, { "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", "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "O", "B-COLUMN", "O" ]
1,304
student_club
bird:dev.json:1375
List all the members of the "School of Applied Sciences, Technology and Education" department.
SELECT T1.first_name, T1.last_name FROM member AS T1 INNER JOIN major AS T2 ON T1.link_to_major = T2.major_id WHERE T2.department = 'School of Applied Sciences, Technology and Education'
[ "List", "all", "the", "members", "of", "the", "\"", "School", "of", "Applied", "Sciences", ",", "Technology", "and", "Education", "\"", "department", "." ]
[ { "id": 5, "type": "value", "value": "School of Applied Sciences, Technology and Education" }, { "id": 6, "type": "column", "value": "link_to_major" }, { "id": 0, "type": "column", "value": "first_name" }, { "id": 4, "type": "column", "value": "department" }, { "id": 1, "type": "column", "value": "last_name" }, { "id": 7, "type": "column", "value": "major_id" }, { "id": 2, "type": "table", "value": "member" }, { "id": 3, "type": "table", "value": "major" } ]
[ { "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": [ 16 ] }, { "entity_id": 5, "token_idxs": [ 7, 8, 9, 10, 11, 12, 13, 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", "O", "B-TABLE", "O", "O", "O", "B-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "O", "B-COLUMN", "O" ]
1,305
institution_sports
bird:test.json:1673
List the nicknames of institutions in descending order of capacity.
SELECT T1.Nickname FROM championship AS T1 JOIN institution AS T2 ON T1.Institution_ID = T2.Institution_ID ORDER BY T2.Capacity DESC
[ "List", "the", "nicknames", "of", "institutions", "in", "descending", "order", "of", "capacity", "." ]
[ { "id": 4, "type": "column", "value": "institution_id" }, { "id": 1, "type": "table", "value": "championship" }, { "id": 2, "type": "table", "value": "institution" }, { "id": 0, "type": "column", "value": "nickname" }, { "id": 3, "type": "column", "value": "capacity" } ]
[ { "entity_id": 0, "token_idxs": [ 2 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 4 ] }, { "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-COLUMN", "O" ]
1,306
vehicle_rent
bird:test.json:417
Show the name and age of the customer with maximum membership credit.
SELECT name , age FROM customers ORDER BY membership_credit DESC LIMIT 1
[ "Show", "the", "name", "and", "age", "of", "the", "customer", "with", "maximum", "membership", "credit", "." ]
[ { "id": 3, "type": "column", "value": "membership_credit" }, { "id": 0, "type": "table", "value": "customers" }, { "id": 1, "type": "column", "value": "name" }, { "id": 2, "type": "column", "value": "age" } ]
[ { "entity_id": 0, "token_idxs": [ 7 ] }, { "entity_id": 1, "token_idxs": [ 2 ] }, { "entity_id": 2, "token_idxs": [ 4 ] }, { "entity_id": 3, "token_idxs": [ 10, 11 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-COLUMN", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "B-COLUMN", "I-COLUMN", "O" ]
1,308
soccer_3
bird:test.json:29
Which manufacturers work for more than 1 club?
SELECT Manufacturer FROM club GROUP BY Manufacturer HAVING COUNT(*) > 1
[ "Which", "manufacturers", "work", "for", "more", "than", "1", "club", "?" ]
[ { "id": 1, "type": "column", "value": "manufacturer" }, { "id": 0, "type": "table", "value": "club" }, { "id": 2, "type": "value", "value": "1" } ]
[ { "entity_id": 0, "token_idxs": [ 7 ] }, { "entity_id": 1, "token_idxs": [ 1 ] }, { "entity_id": 2, "token_idxs": [ 6 ] }, { "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", "B-VALUE", "B-TABLE", "O" ]
1,309
codebase_comments
bird:train.json:575
Which repository has the longest amount of processed time of downloading? Indicate whether the solution paths in the repository can be implemented without needs of compilation.
SELECT DISTINCT T1.id, T2.WasCompiled FROM Repo AS T1 INNER JOIN Solution AS T2 ON T1.Id = T2.RepoId WHERE T1.ProcessedTime = ( SELECT MAX(ProcessedTime) FROM Repo )
[ "Which", "repository", "has", "the", "longest", "amount", "of", "processed", "time", "of", "downloading", "?", "Indicate", "whether", "the", "solution", "paths", "in", "the", "repository", "can", "be", "implemented", "without", "needs", "of", "compilation", "." ]
[ { "id": 4, "type": "column", "value": "processedtime" }, { "id": 1, "type": "column", "value": "wascompiled" }, { "id": 3, "type": "table", "value": "solution" }, { "id": 5, "type": "column", "value": "repoid" }, { "id": 2, "type": "table", "value": "repo" }, { "id": 0, "type": "column", "value": "id" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 15 ] }, { "entity_id": 4, "token_idxs": [ 7, 8 ] }, { "entity_id": 5, "token_idxs": [ 1 ] }, { "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-COLUMN", "I-COLUMN", "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O" ]
1,310
mondial_geo
bird:train.json:8327
What is the average population for all cities location at Baltic Sea?
SELECT AVG(T1.Population) FROM city AS T1 INNER JOIN located AS T2 ON T1.Name = T2.City INNER JOIN sea AS T3 ON T3.Name = T2.Sea WHERE T3.Name = 'Baltic Sea'
[ "What", "is", "the", "average", "population", "for", "all", "cities", "location", "at", "Baltic", "Sea", "?" ]
[ { "id": 2, "type": "value", "value": "Baltic Sea" }, { "id": 3, "type": "column", "value": "population" }, { "id": 5, "type": "table", "value": "located" }, { "id": 1, "type": "column", "value": "name" }, { "id": 4, "type": "table", "value": "city" }, { "id": 7, "type": "column", "value": "city" }, { "id": 0, "type": "table", "value": "sea" }, { "id": 6, "type": "column", "value": "sea" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 10 ] }, { "entity_id": 3, "token_idxs": [ 4 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [ 8 ] }, { "entity_id": 6, "token_idxs": [ 11 ] }, { "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": [] } ]
[ "O", "O", "O", "O", "B-COLUMN", "O", "O", "B-COLUMN", "B-TABLE", "O", "B-VALUE", "B-COLUMN", "O" ]
1,311
law_episode
bird:train.json:1352
Who was the actor who was portraying "Alex Brown" and has been credited?
SELECT T1.name FROM Person AS T1 INNER JOIN Credit AS T2 ON T1.person_id = T2.person_id WHERE T2.role = 'Alex Brown' AND T2.credited = 'true'
[ "Who", "was", "the", "actor", "who", "was", "portraying", "\"", "Alex", "Brown", "\"", "and", "has", "been", "credited", "?" ]
[ { "id": 5, "type": "value", "value": "Alex Brown" }, { "id": 3, "type": "column", "value": "person_id" }, { "id": 6, "type": "column", "value": "credited" }, { "id": 1, "type": "table", "value": "person" }, { "id": 2, "type": "table", "value": "credit" }, { "id": 0, "type": "column", "value": "name" }, { "id": 4, "type": "column", "value": "role" }, { "id": 7, "type": "value", "value": "true" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "O", "B-VALUE", "I-VALUE", "O", "O", "O", "O", "B-TABLE", "O" ]
1,313
customers_and_addresses
spider:train_spider.json:6092
What are the state and country of all the cities that have post codes starting with 4.\
SELECT state_province_county , country FROM addresses WHERE zip_postcode LIKE "4%"
[ "What", "are", "the", "state", "and", "country", "of", "all", "the", "cities", "that", "have", "post", "codes", "starting", "with", "4.\\" ]
[ { "id": 1, "type": "column", "value": "state_province_county" }, { "id": 3, "type": "column", "value": "zip_postcode" }, { "id": 0, "type": "table", "value": "addresses" }, { "id": 2, "type": "column", "value": "country" }, { "id": 4, "type": "column", "value": "4%" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 3, 4 ] }, { "entity_id": 2, "token_idxs": [ 5 ] }, { "entity_id": 3, "token_idxs": [ 12, 13 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-COLUMN", "I-COLUMN", "B-COLUMN", "O", "O", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "O" ]
1,314
college_2
spider:train_spider.json:1330
Find the name and building of the department with the highest budget.
SELECT dept_name , building FROM department ORDER BY budget DESC LIMIT 1
[ "Find", "the", "name", "and", "building", "of", "the", "department", "with", "the", "highest", "budget", "." ]
[ { "id": 0, "type": "table", "value": "department" }, { "id": 1, "type": "column", "value": "dept_name" }, { "id": 2, "type": "column", "value": "building" }, { "id": 3, "type": "column", "value": "budget" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 7 ] }, { "entity_id": 2, "token_idxs": [ 4 ] }, { "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", "O", "O", "B-COLUMN", "O", "O", "B-COLUMN", "O", "O", "O", "B-COLUMN", "O" ]
1,315
citeseer
bird:train.json:4154
How many papers were cited by schmidt99advanced cited word3555?
SELECT COUNT(T2.paper_id) FROM cites AS T1 INNER JOIN content AS T2 ON T1.cited_paper_id = T2.paper_id WHERE T1.citing_paper_id = 'schmidt99advanced' AND T2.word_cited_id = 'word3555'
[ "How", "many", "papers", "were", "cited", "by", "schmidt99advanced", "cited", "word3555", "?" ]
[ { "id": 5, "type": "value", "value": "schmidt99advanced" }, { "id": 4, "type": "column", "value": "citing_paper_id" }, { "id": 3, "type": "column", "value": "cited_paper_id" }, { "id": 6, "type": "column", "value": "word_cited_id" }, { "id": 2, "type": "column", "value": "paper_id" }, { "id": 7, "type": "value", "value": "word3555" }, { "id": 1, "type": "table", "value": "content" }, { "id": 0, "type": "table", "value": "cites" } ]
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[ "O", "O", "B-COLUMN", "B-COLUMN", "B-TABLE", "O", "B-VALUE", "O", "B-VALUE", "O" ]
1,316
epinions_1
spider:train_spider.json:1705
List the titles of all items in alphabetic order .
SELECT title FROM item ORDER BY title
[ "List", "the", "titles", "of", "all", "items", "in", "alphabetic", "order", "." ]
[ { "id": 1, "type": "column", "value": "title" }, { "id": 0, "type": "table", "value": "item" } ]
[ { "entity_id": 0, "token_idxs": [ 5 ] }, { "entity_id": 1, "token_idxs": [ 2 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "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" ]
1,317
chinook_1
spider:train_spider.json:847
How many customers have email that contains "gmail.com"?
SELECT COUNT(*) FROM CUSTOMER WHERE Email LIKE "%gmail.com%"
[ "How", "many", "customers", "have", "email", "that", "contains", "\"", "gmail.com", "\"", "?" ]
[ { "id": 2, "type": "column", "value": "%gmail.com%" }, { "id": 0, "type": "table", "value": "customer" }, { "id": 1, "type": "column", "value": "email" } ]
[ { "entity_id": 0, "token_idxs": [ 2 ] }, { "entity_id": 1, "token_idxs": [ 4 ] }, { "entity_id": 2, "token_idxs": [ 8 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-TABLE", "O", "B-COLUMN", "O", "O", "O", "B-COLUMN", "O", "O" ]
1,318
movies_4
bird:train.json:505
List down the movies produced by Lucasfilm.
SELECT T3.title FROM production_company AS T1 INNER JOIN movie_company AS T2 ON T1.company_id = T2.company_id INNER JOIN movie AS T3 ON T2.movie_id = T3.movie_id WHERE T1.company_name = 'Lucasfilm'
[ "List", "down", "the", "movies", "produced", "by", "Lucasfilm", "." ]
[ { "id": 4, "type": "table", "value": "production_company" }, { "id": 5, "type": "table", "value": "movie_company" }, { "id": 2, "type": "column", "value": "company_name" }, { "id": 7, "type": "column", "value": "company_id" }, { "id": 3, "type": "value", "value": "Lucasfilm" }, { "id": 6, "type": "column", "value": "movie_id" }, { "id": 0, "type": "column", "value": "title" }, { "id": 1, "type": "table", "value": "movie" } ]
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[ "O", "O", "O", "B-TABLE", "O", "O", "B-VALUE", "O" ]
1,319
professional_basketball
bird:train.json:2857
Please list the top three shortest black players.
SELECT firstName, lastName FROM players WHERE race = 'B' AND height > 0 ORDER BY height ASC LIMIT 3
[ "Please", "list", "the", "top", "three", "shortest", "black", "players", "." ]
[ { "id": 1, "type": "column", "value": "firstname" }, { "id": 2, "type": "column", "value": "lastname" }, { "id": 0, "type": "table", "value": "players" }, { "id": 3, "type": "column", "value": "height" }, { "id": 4, "type": "column", "value": "race" }, { "id": 5, "type": "value", "value": "B" }, { "id": 6, "type": "value", "value": "0" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "O" ]
1,321
synthea
bird:train.json:1432
List down the first name of patients who encountered normal pregnancy.
SELECT DISTINCT T1.first FROM patients AS T1 INNER JOIN encounters AS T2 ON T1.patient = T2.PATIENT WHERE T2.REASONDESCRIPTION = 'Normal pregnancy'
[ "List", "down", "the", "first", "name", "of", "patients", "who", "encountered", "normal", "pregnancy", "." ]
[ { "id": 3, "type": "column", "value": "reasondescription" }, { "id": 4, "type": "value", "value": "Normal pregnancy" }, { "id": 2, "type": "table", "value": "encounters" }, { "id": 1, "type": "table", "value": "patients" }, { "id": 5, "type": "column", "value": "patient" }, { "id": 0, "type": "column", "value": "first" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "O", "B-COLUMN", "O", "B-TABLE", "B-VALUE", "I-VALUE", "O" ]
1,322
student_loan
bird:train.json:4554
What are the names of the students who joined the Marines?
SELECT name FROM enlist WHERE organ = 'marines'
[ "What", "are", "the", "names", "of", "the", "students", "who", "joined", "the", "Marines", "?" ]
[ { "id": 3, "type": "value", "value": "marines" }, { "id": 0, "type": "table", "value": "enlist" }, { "id": 2, "type": "column", "value": "organ" }, { "id": 1, "type": "column", "value": "name" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "O", "O", "O", "O", "O", "B-VALUE", "O" ]
1,323
toxicology
bird:dev.json:206
What elements are in the TR004_8_9 bond atoms?
SELECT DISTINCT T1.element FROM atom AS T1 INNER JOIN connected AS T2 ON T1.atom_id = T2.atom_id WHERE T2.bond_id = 'TR004_8_9'
[ "What", "elements", "are", "in", "the", "TR004_8_9", "bond", "atoms", "?" ]
[ { "id": 2, "type": "table", "value": "connected" }, { "id": 4, "type": "value", "value": "TR004_8_9" }, { "id": 0, "type": "column", "value": "element" }, { "id": 3, "type": "column", "value": "bond_id" }, { "id": 5, "type": "column", "value": "atom_id" }, { "id": 1, "type": "table", "value": "atom" } ]
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[ "O", "B-COLUMN", "O", "O", "O", "B-VALUE", "B-COLUMN", "B-TABLE", "O" ]
1,325
city_record
spider:train_spider.json:6306
What are the GDP and population of the city that already served as a host more than once?
SELECT t1.gdp , t1.Regional_Population FROM city AS T1 JOIN hosting_city AS T2 ON T1.city_id = T2.host_city GROUP BY t2.Host_City HAVING count(*) > 1
[ "What", "are", "the", "GDP", "and", "population", "of", "the", "city", "that", "already", "served", "as", "a", "host", "more", "than", "once", "?" ]
[ { "id": 2, "type": "column", "value": "regional_population" }, { "id": 4, "type": "table", "value": "hosting_city" }, { "id": 0, "type": "column", "value": "host_city" }, { "id": 6, "type": "column", "value": "city_id" }, { "id": 3, "type": "table", "value": "city" }, { "id": 1, "type": "column", "value": "gdp" }, { "id": 5, "type": "value", "value": "1" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O" ]
1,326
software_company
bird:train.json:8561
List down the geographic identifier with an number of inhabitants less than 30.
SELECT GEOID FROM Demog WHERE INHABITANTS_K < 30
[ "List", "down", "the", "geographic", "identifier", "with", "an", "number", "of", "inhabitants", "less", "than", "30", "." ]
[ { "id": 2, "type": "column", "value": "inhabitants_k" }, { "id": 0, "type": "table", "value": "demog" }, { "id": 1, "type": "column", "value": "geoid" }, { "id": 3, "type": "value", "value": "30" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "B-VALUE", "O" ]
1,327
works_cycles
bird:train.json:7033
What are the names of the product that has the lowest rating?
SELECT T2.Name FROM ProductReview AS T1 INNER JOIN Product AS T2 ON T1.ProductID = T2.ProductID WHERE T1.Rating = ( SELECT Rating FROM ProductReview ORDER BY Rating ASC LIMIT 1 )
[ "What", "are", "the", "names", "of", "the", "product", "that", "has", "the", "lowest", "rating", "?" ]
[ { "id": 1, "type": "table", "value": "productreview" }, { "id": 4, "type": "column", "value": "productid" }, { "id": 2, "type": "table", "value": "product" }, { "id": 3, "type": "column", "value": "rating" }, { "id": 0, "type": "column", "value": "name" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "O", "O", "B-COLUMN", "O" ]
1,328
video_games
bird:train.json:3371
Give the name of the publisher of the game ID 75.
SELECT T2.publisher_name FROM game_publisher AS T1 INNER JOIN publisher AS T2 ON T1.publisher_id = T2.id WHERE T1.game_id = 75
[ "Give", "the", "name", "of", "the", "publisher", "of", "the", "game", "ID", "75", "." ]
[ { "id": 0, "type": "column", "value": "publisher_name" }, { "id": 1, "type": "table", "value": "game_publisher" }, { "id": 5, "type": "column", "value": "publisher_id" }, { "id": 2, "type": "table", "value": "publisher" }, { "id": 3, "type": "column", "value": "game_id" }, { "id": 4, "type": "value", "value": "75" }, { "id": 6, "type": "column", "value": "id" } ]
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[ "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "B-COLUMN", "B-COLUMN", "B-VALUE", "O" ]
1,329
simpson_episodes
bird:train.json:4367
In year 2009, what is the percentage of the episode titled by "Gone Maggie Gone" being nominated?
SELECT CAST((SUM(CASE WHEN T1.result = 'Nominee' THEN 1 ELSE 0 END) - SUM(CASE WHEN T1.result = 'Winner' THEN 1 ELSE 0 END)) AS REAL) * 100 / COUNT(T1.result) FROM Award AS T1 INNER JOIN Episode AS T2 ON T1.episode_id = T2.episode_id WHERE T2.title = 'Gone Maggie Gone' AND T1.year = 2009;
[ "In", "year", "2009", ",", "what", "is", "the", "percentage", "of", "the", "episode", "titled", "by", "\"", "Gone", "Maggie", "Gone", "\"", "being", "nominated", "?" ]
[ { "id": 4, "type": "value", "value": "Gone Maggie Gone" }, { "id": 2, "type": "column", "value": "episode_id" }, { "id": 1, "type": "table", "value": "episode" }, { "id": 11, "type": "value", "value": "Nominee" }, { "id": 8, "type": "column", "value": "result" }, { "id": 12, "type": "value", "value": "Winner" }, { "id": 0, "type": "table", "value": "award" }, { "id": 3, "type": "column", "value": "title" }, { "id": 5, "type": "column", "value": "year" }, { "id": 6, "type": "value", "value": "2009" }, { "id": 7, "type": "value", "value": "100" }, { "id": 9, "type": "value", "value": "0" }, { "id": 10, "type": "value", "value": "1" } ]
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[ "B-VALUE", "B-COLUMN", "B-VALUE", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "B-COLUMN", "O", "O", "B-VALUE", "I-VALUE", "I-VALUE", "O", "O", "B-VALUE", "O" ]
1,330
card_games
bird:dev.json:465
For the set of cards with "Ancestor's Chosen" in it, is there a Korean version of it?
SELECT IIF(SUM(CASE WHEN T2.language = 'Korean' AND T2.translation IS NOT NULL THEN 1 ELSE 0 END) > 0, 'YES', 'NO') FROM cards AS T1 INNER JOIN set_translations AS T2 ON T2.setCode = T1.setCode WHERE T1.name = 'Ancestor''s Chosen'
[ "For", "the", "set", "of", "cards", "with", "\"", "Ancestor", "'s", "Chosen", "\"", "in", "it", ",", "is", "there", "a", "Korean", "version", "of", "it", "?" ]
[ { "id": 3, "type": "value", "value": "Ancestor's Chosen" }, { "id": 1, "type": "table", "value": "set_translations" }, { "id": 11, "type": "column", "value": "translation" }, { "id": 9, "type": "column", "value": "language" }, { "id": 6, "type": "column", "value": "setcode" }, { "id": 10, "type": "value", "value": "Korean" }, { "id": 0, "type": "table", "value": "cards" }, { "id": 2, "type": "column", "value": "name" }, { "id": 4, "type": "value", "value": "YES" }, { "id": 5, "type": "value", "value": "NO" }, { "id": 7, "type": "value", "value": "0" }, { "id": 8, "type": "value", "value": "1" } ]
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[ "O", "O", "B-COLUMN", "I-COLUMN", "B-TABLE", "O", "O", "B-VALUE", "I-VALUE", "I-VALUE", "O", "O", "O", "O", "O", "O", "O", "B-VALUE", "O", "O", "O", "O" ]
1,331
sakila_1
spider:train_spider.json:2943
What are the countries that contain 3 or more cities?
SELECT T2.country FROM city AS T1 JOIN country AS T2 ON T1.country_id = T2.country_id GROUP BY T2.country_id HAVING count(*) >= 3
[ "What", "are", "the", "countries", "that", "contain", "3", "or", "more", "cities", "?" ]
[ { "id": 0, "type": "column", "value": "country_id" }, { "id": 1, "type": "column", "value": "country" }, { "id": 3, "type": "table", "value": "country" }, { "id": 2, "type": "table", "value": "city" }, { "id": 4, "type": "value", "value": "3" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 9 ] }, { "entity_id": 3, "token_idxs": [ 3 ] }, { "entity_id": 4, "token_idxs": [ 6 ] }, { "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", "B-VALUE", "O", "O", "B-TABLE", "O" ]
1,332
products_for_hire
spider:train_spider.json:1969
What are the first name, last name, and gender of all the good customers? Order by their last name.
SELECT first_name , last_name , gender_mf FROM customers WHERE good_or_bad_customer = 'good' ORDER BY last_name
[ "What", "are", "the", "first", "name", ",", "last", "name", ",", "and", "gender", "of", "all", "the", "good", "customers", "?", "Order", "by", "their", "last", "name", "." ]
[ { "id": 4, "type": "column", "value": "good_or_bad_customer" }, { "id": 1, "type": "column", "value": "first_name" }, { "id": 0, "type": "table", "value": "customers" }, { "id": 2, "type": "column", "value": "last_name" }, { "id": 3, "type": "column", "value": "gender_mf" }, { "id": 5, "type": "value", "value": "good" } ]
[ { "entity_id": 0, "token_idxs": [ 15 ] }, { "entity_id": 1, "token_idxs": [ 3, 4 ] }, { "entity_id": 2, "token_idxs": [ 6, 7 ] }, { "entity_id": 3, "token_idxs": [ 10 ] }, { "entity_id": 4, "token_idxs": [] }, { "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", "O", "B-COLUMN", "I-COLUMN", "O", "B-COLUMN", "I-COLUMN", "O", "O", "B-COLUMN", "O", "O", "O", "B-VALUE", "B-TABLE", "O", "O", "O", "O", "O", "O", "O" ]
1,333
codebase_comments
bird:train.json:610
For the method which got the tokenized name as 'interp parser expr', what is the processed time for its solution?
SELECT T1.ProcessedTime FROM Solution AS T1 INNER JOIN Method AS T2 ON T1.Id = T2.SolutionId WHERE T2.NameTokenized = 'interp parser expr'
[ "For", "the", "method", "which", "got", "the", "tokenized", "name", "as", "'", "interp", "parser", "expr", "'", ",", "what", "is", "the", "processed", "time", "for", "its", "solution", "?" ]
[ { "id": 4, "type": "value", "value": "interp parser expr" }, { "id": 0, "type": "column", "value": "processedtime" }, { "id": 3, "type": "column", "value": "nametokenized" }, { "id": 6, "type": "column", "value": "solutionid" }, { "id": 1, "type": "table", "value": "solution" }, { "id": 2, "type": "table", "value": "method" }, { "id": 5, "type": "column", "value": "id" } ]
[ { "entity_id": 0, "token_idxs": [ 18, 19 ] }, { "entity_id": 1, "token_idxs": [ 22 ] }, { "entity_id": 2, "token_idxs": [ 2 ] }, { "entity_id": 3, "token_idxs": [ 6 ] }, { "entity_id": 4, "token_idxs": [ 10, 11, 12 ] }, { "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-COLUMN", "O", "O", "O", "B-VALUE", "I-VALUE", "I-VALUE", "O", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "B-TABLE", "O" ]
1,334
csu_1
spider:train_spider.json:2368
How many degrees were conferred in "San Jose State University" in 2000?
SELECT degrees FROM campuses AS T1 JOIN degrees AS T2 ON t1.id = t2.campus WHERE t1.campus = "San Jose State University" AND t2.year = 2000
[ "How", "many", "degrees", "were", "conferred", "in", "\"", "San", "Jose", "State", "University", "\"", "in", "2000", "?" ]
[ { "id": 5, "type": "column", "value": "San Jose State University" }, { "id": 1, "type": "table", "value": "campuses" }, { "id": 0, "type": "column", "value": "degrees" }, { "id": 2, "type": "table", "value": "degrees" }, { "id": 4, "type": "column", "value": "campus" }, { "id": 6, "type": "column", "value": "year" }, { "id": 7, "type": "value", "value": "2000" }, { "id": 3, "type": "column", "value": "id" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 2 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [ 7, 8, 9, 10 ] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [ 13 ] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-TABLE", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "I-COLUMN", "O", "O", "B-VALUE", "O" ]
1,335
college_2
spider:train_spider.json:1434
Find the name of instructors who are advisors of the students from the Math department, and sort the results by students' total credit.
SELECT T2.name FROM advisor AS T1 JOIN instructor AS T2 ON T1.i_id = T2.id JOIN student AS T3 ON T1.s_id = T3.id WHERE T3.dept_name = 'Math' ORDER BY T3.tot_cred
[ "Find", "the", "name", "of", "instructors", "who", "are", "advisors", "of", "the", "students", "from", "the", "Math", "department", ",", "and", "sort", "the", "results", "by", "students", "'", "total", "credit", "." ]
[ { "id": 6, "type": "table", "value": "instructor" }, { "id": 2, "type": "column", "value": "dept_name" }, { "id": 4, "type": "column", "value": "tot_cred" }, { "id": 1, "type": "table", "value": "student" }, { "id": 5, "type": "table", "value": "advisor" }, { "id": 0, "type": "column", "value": "name" }, { "id": 3, "type": "value", "value": "Math" }, { "id": 7, "type": "column", "value": "s_id" }, { "id": 9, "type": "column", "value": "i_id" }, { "id": 8, "type": "column", "value": "id" } ]
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[ "B-COLUMN", "O", "B-COLUMN", "O", "B-TABLE", "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "B-VALUE", "B-COLUMN", "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "B-COLUMN", "I-COLUMN", "O" ]
1,336
advertising_agencies
bird:test.json:2104
What are all payment ids and payment details for invoices with status Working?
SELECT T1.payment_id , T1.payment_details FROM Payments AS T1 JOIN Invoices AS T2 ON T1.invoice_id = T2.invoice_id WHERE T2.invoice_status = 'Working'
[ "What", "are", "all", "payment", "ids", "and", "payment", "details", "for", "invoices", "with", "status", "Working", "?" ]
[ { "id": 1, "type": "column", "value": "payment_details" }, { "id": 4, "type": "column", "value": "invoice_status" }, { "id": 0, "type": "column", "value": "payment_id" }, { "id": 6, "type": "column", "value": "invoice_id" }, { "id": 2, "type": "table", "value": "payments" }, { "id": 3, "type": "table", "value": "invoices" }, { "id": 5, "type": "value", "value": "Working" } ]
[ { "entity_id": 0, "token_idxs": [ 3, 4 ] }, { "entity_id": 1, "token_idxs": [ 7 ] }, { "entity_id": 2, "token_idxs": [ 6 ] }, { "entity_id": 3, "token_idxs": [ 9 ] }, { "entity_id": 4, "token_idxs": [ 10, 11 ] }, { "entity_id": 5, "token_idxs": [ 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", "B-COLUMN", "I-COLUMN", "O", "B-TABLE", "B-COLUMN", "O", "B-TABLE", "B-COLUMN", "I-COLUMN", "B-VALUE", "O" ]
1,337
address_1
bird:test.json:825
What is the average distance between Boston and all other cities.
SELECT avg(distance) FROM Direct_distance AS T1 JOIN City AS T2 ON T1.city1_code = T2.city_code WHERE T2.city_name = "Boston"
[ "What", "is", "the", "average", "distance", "between", "Boston", "and", "all", "other", "cities", "." ]
[ { "id": 0, "type": "table", "value": "direct_distance" }, { "id": 5, "type": "column", "value": "city1_code" }, { "id": 2, "type": "column", "value": "city_name" }, { "id": 6, "type": "column", "value": "city_code" }, { "id": 4, "type": "column", "value": "distance" }, { "id": 3, "type": "column", "value": "Boston" }, { "id": 1, "type": "table", "value": "city" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 10 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 6 ] }, { "entity_id": 4, "token_idxs": [ 4 ] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "B-COLUMN", "O", "B-COLUMN", "O", "O", "O", "B-TABLE", "O" ]
1,338
codebase_community
bird:dev.json:699
When did the user known as 'IrishStat' create his or her account?
SELECT CreationDate FROM users WHERE DisplayName = 'IrishStat'
[ "When", "did", "the", "user", "known", "as", "'", "IrishStat", "'", "create", "his", "or", "her", "account", "?" ]
[ { "id": 1, "type": "column", "value": "creationdate" }, { "id": 2, "type": "column", "value": "displayname" }, { "id": 3, "type": "value", "value": "IrishStat" }, { "id": 0, "type": "table", "value": "users" } ]
[ { "entity_id": 0, "token_idxs": [ 3 ] }, { "entity_id": 1, "token_idxs": [ 9 ] }, { "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-TABLE", "O", "O", "O", "B-VALUE", "O", "B-COLUMN", "O", "O", "O", "O", "O" ]
1,339
movie_3
bird:train.json:9358
Calculate the total rental rate for animation film titles.
SELECT SUM(T1.rental_rate) FROM film AS T1 INNER JOIN film_category AS T2 ON T1.film_id = T2.film_id INNER JOIN category AS T3 ON T2.category_id = T3.category_id WHERE T3.`name` = 'Animation'
[ "Calculate", "the", "total", "rental", "rate", "for", "animation", "film", "titles", "." ]
[ { "id": 5, "type": "table", "value": "film_category" }, { "id": 3, "type": "column", "value": "rental_rate" }, { "id": 6, "type": "column", "value": "category_id" }, { "id": 2, "type": "value", "value": "Animation" }, { "id": 0, "type": "table", "value": "category" }, { "id": 7, "type": "column", "value": "film_id" }, { "id": 1, "type": "column", "value": "name" }, { "id": 4, "type": "table", "value": "film" } ]
[ { "entity_id": 0, "token_idxs": [ 4, 5 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 6 ] }, { "entity_id": 3, "token_idxs": [ 3 ] }, { "entity_id": 4, "token_idxs": [ 7 ] }, { "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", "B-TABLE", "I-TABLE", "B-VALUE", "B-TABLE", "O", "O" ]
1,340
wine_1
spider:train_spider.json:6535
Find the names of all distinct wines that have appellations in North Coast area.
SELECT DISTINCT T2.Name FROM APPELLATIONs AS T1 JOIN WINE AS T2 ON T1.Appelation = T2.Appelation WHERE T1.Area = "North Coast"
[ "Find", "the", "names", "of", "all", "distinct", "wines", "that", "have", "appellations", "in", "North", "Coast", "area", "." ]
[ { "id": 1, "type": "table", "value": "appellations" }, { "id": 4, "type": "column", "value": "North Coast" }, { "id": 5, "type": "column", "value": "appelation" }, { "id": 0, "type": "column", "value": "name" }, { "id": 2, "type": "table", "value": "wine" }, { "id": 3, "type": "column", "value": "area" } ]
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[ "O", "O", "B-COLUMN", "O", "O", "O", "B-TABLE", "O", "O", "B-COLUMN", "O", "B-COLUMN", "I-COLUMN", "B-COLUMN", "O" ]
1,342
driving_school
spider:train_spider.json:6645
In which country and state does Janessa Sawayn live?
SELECT T1.country , T1.state_province_county FROM Addresses AS T1 JOIN Staff AS T2 ON T1.address_id = T2.staff_address_id WHERE T2.first_name = "Janessa" AND T2.last_name = "Sawayn";
[ "In", "which", "country", "and", "state", "does", "Janessa", "Sawayn", "live", "?" ]
[ { "id": 1, "type": "column", "value": "state_province_county" }, { "id": 5, "type": "column", "value": "staff_address_id" }, { "id": 4, "type": "column", "value": "address_id" }, { "id": 6, "type": "column", "value": "first_name" }, { "id": 2, "type": "table", "value": "addresses" }, { "id": 8, "type": "column", "value": "last_name" }, { "id": 0, "type": "column", "value": "country" }, { "id": 7, "type": "column", "value": "Janessa" }, { "id": 9, "type": "column", "value": "Sawayn" }, { "id": 3, "type": "table", "value": "staff" } ]
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[ "O", "O", "B-COLUMN", "O", "B-TABLE", "O", "B-COLUMN", "B-COLUMN", "O", "O" ]
1,344
video_games
bird:train.json:3448
How many FIFA games are there across all platforms?
SELECT COUNT(*) FROM ( SELECT T.game_name FROM game AS T WHERE T.game_name LIKE '%FIFA%' )
[ "How", "many", "FIFA", "games", "are", "there", "across", "all", "platforms", "?" ]
[ { "id": 1, "type": "column", "value": "game_name" }, { "id": 2, "type": "value", "value": "%FIFA%" }, { "id": 0, "type": "table", "value": "game" } ]
[ { "entity_id": 0, "token_idxs": [ 3 ] }, { "entity_id": 1, "token_idxs": [ 4 ] }, { "entity_id": 2, "token_idxs": [ 2 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-VALUE", "B-TABLE", "B-COLUMN", "O", "O", "O", "O", "O" ]
1,345
college_1
spider:train_spider.json:3201
What is the total credit does each department offer?
SELECT sum(crs_credit) , dept_code FROM course GROUP BY dept_code
[ "What", "is", "the", "total", "credit", "does", "each", "department", "offer", "?" ]
[ { "id": 2, "type": "column", "value": "crs_credit" }, { "id": 1, "type": "column", "value": "dept_code" }, { "id": 0, "type": "table", "value": "course" } ]
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[ "O", "O", "O", "O", "B-COLUMN", "O", "O", "O", "O", "O" ]
1,346
legislator
bird:train.json:4800
List the IDs and full names of legislators from the Liberal Republican party.
SELECT T2.bioguide_id, T2.first_name, T2.last_name FROM `historical-terms` AS T1 INNER JOIN historical AS T2 ON T2.bioguide_id = T1.bioguide WHERE T1.party = 'Liberal Republican'
[ "List", "the", "IDs", "and", "full", "names", "of", "legislators", "from", "the", "Liberal", "Republican", "party", "." ]
[ { "id": 6, "type": "value", "value": "Liberal Republican" }, { "id": 3, "type": "table", "value": "historical-terms" }, { "id": 0, "type": "column", "value": "bioguide_id" }, { "id": 1, "type": "column", "value": "first_name" }, { "id": 4, "type": "table", "value": "historical" }, { "id": 2, "type": "column", "value": "last_name" }, { "id": 7, "type": "column", "value": "bioguide" }, { "id": 5, "type": "column", "value": "party" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-VALUE", "I-VALUE", "B-COLUMN", "O" ]
1,347
game_1
spider:train_spider.json:6010
What is the total number of all football games played by scholarship students?
SELECT sum(gamesplayed) FROM Sportsinfo WHERE sportname = "Football" AND onscholarship = 'Y'
[ "What", "is", "the", "total", "number", "of", "all", "football", "games", "played", "by", "scholarship", "students", "?" ]
[ { "id": 4, "type": "column", "value": "onscholarship" }, { "id": 1, "type": "column", "value": "gamesplayed" }, { "id": 0, "type": "table", "value": "sportsinfo" }, { "id": 2, "type": "column", "value": "sportname" }, { "id": 3, "type": "column", "value": "Football" }, { "id": 5, "type": "value", "value": "Y" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "B-COLUMN", "I-COLUMN", "B-VALUE", "B-COLUMN", "O", "O" ]
1,348
sports_competition
spider:train_spider.json:3379
what are the name of players who get more than the average points.
SELECT name FROM player WHERE points > (SELECT avg(points) FROM player)
[ "what", "are", "the", "name", "of", "players", "who", "get", "more", "than", "the", "average", "points", "." ]
[ { "id": 0, "type": "table", "value": "player" }, { "id": 2, "type": "column", "value": "points" }, { "id": 1, "type": "column", "value": "name" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O" ]
1,349
driving_school
spider:train_spider.json:6638
What is the nickname of staff with first name as Janessa and last name as Sawayn?
SELECT nickname FROM Staff WHERE first_name = "Janessa" AND last_name = "Sawayn";
[ "What", "is", "the", "nickname", "of", "staff", "with", "first", "name", "as", "Janessa", "and", "last", "name", "as", "Sawayn", "?" ]
[ { "id": 2, "type": "column", "value": "first_name" }, { "id": 4, "type": "column", "value": "last_name" }, { "id": 1, "type": "column", "value": "nickname" }, { "id": 3, "type": "column", "value": "Janessa" }, { "id": 5, "type": "column", "value": "Sawayn" }, { "id": 0, "type": "table", "value": "staff" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "B-TABLE", "O", "B-COLUMN", "I-COLUMN", "O", "B-COLUMN", "O", "B-COLUMN", "I-COLUMN", "O", "B-COLUMN", "O" ]
1,350
cre_Docs_and_Epenses
spider:train_spider.json:6399
Show the number of documents.
SELECT count(*) FROM Documents
[ "Show", "the", "number", "of", "documents", "." ]
[ { "id": 0, "type": "table", "value": "documents" } ]
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[ "O", "O", "O", "O", "B-TABLE", "O" ]
1,351
scientist_1
spider:train_spider.json:6510
How many scientists do not have any projects assigned to them?
SELECT count(*) FROM scientists WHERE ssn NOT IN (SELECT scientist FROM AssignedTo)
[ "How", "many", "scientists", "do", "not", "have", "any", "projects", "assigned", "to", "them", "?" ]
[ { "id": 0, "type": "table", "value": "scientists" }, { "id": 2, "type": "table", "value": "assignedto" }, { "id": 3, "type": "column", "value": "scientist" }, { "id": 1, "type": "column", "value": "ssn" } ]
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[ "O", "O", "B-COLUMN", "O", "O", "O", "O", "O", "B-TABLE", "I-TABLE", "O", "O" ]
1,352
card_games
bird:dev.json:357
What type of promotion is of card 'Duress'?
SELECT promoTypes FROM cards WHERE name = 'Duress' AND promoTypes IS NOT NULL
[ "What", "type", "of", "promotion", "is", "of", "card", "'", "Duress", "'", "?" ]
[ { "id": 1, "type": "column", "value": "promotypes" }, { "id": 3, "type": "value", "value": "Duress" }, { "id": 0, "type": "table", "value": "cards" }, { "id": 2, "type": "column", "value": "name" } ]
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[ "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "B-TABLE", "O", "B-VALUE", "O", "O" ]
1,353
flight_1
spider:train_spider.json:429
Show names for all employees who do not have certificate of Boeing 737-800.
SELECT name FROM Employee EXCEPT SELECT T1.name FROM Employee AS T1 JOIN Certificate AS T2 ON T1.eid = T2.eid JOIN Aircraft AS T3 ON T3.aid = T2.aid WHERE T3.name = "Boeing 737-800"
[ "Show", "names", "for", "all", "employees", "who", "do", "not", "have", "certificate", "of", "Boeing", "737", "-", "800", "." ]
[ { "id": 3, "type": "column", "value": "Boeing 737-800" }, { "id": 4, "type": "table", "value": "certificate" }, { "id": 0, "type": "table", "value": "employee" }, { "id": 2, "type": "table", "value": "aircraft" }, { "id": 1, "type": "column", "value": "name" }, { "id": 5, "type": "column", "value": "aid" }, { "id": 6, "type": "column", "value": "eid" } ]
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[ "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "O", "O", "B-TABLE", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "I-COLUMN", "O" ]
1,354
menu
bird:train.json:5483
How many dishes are there on the menu "Zentral Theater Terrace"?
SELECT SUM(CASE WHEN T3.name = 'Zentral Theater Terrace' THEN 1 ELSE 0 END) FROM MenuItem AS T1 INNER JOIN MenuPage AS T2 ON T1.menu_page_id = T2.id INNER JOIN Menu AS T3 ON T2.menu_id = T3.id
[ "How", "many", "dishes", "are", "there", "on", "the", "menu", "\"", "Zentral", "Theater", "Terrace", "\"", "?" ]
[ { "id": 9, "type": "value", "value": "Zentral Theater Terrace" }, { "id": 6, "type": "column", "value": "menu_page_id" }, { "id": 1, "type": "table", "value": "menuitem" }, { "id": 2, "type": "table", "value": "menupage" }, { "id": 3, "type": "column", "value": "menu_id" }, { "id": 0, "type": "table", "value": "menu" }, { "id": 8, "type": "column", "value": "name" }, { "id": 4, "type": "column", "value": "id" }, { "id": 5, "type": "value", "value": "0" }, { "id": 7, "type": "value", "value": "1" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "B-VALUE", "I-VALUE", "I-VALUE", "O", "O" ]
1,355
public_review_platform
bird:train.json:3944
How many businesses have a romantic ambiance?
SELECT COUNT(T2.business_id) FROM Attributes AS T1 INNER JOIN Business_Attributes AS T2 ON T1.attribute_id = T2.attribute_id WHERE T2.attribute_value = 'true' AND T1.attribute_name = 'ambience_romantic'
[ "How", "many", "businesses", "have", "a", "romantic", "ambiance", "?" ]
[ { "id": 1, "type": "table", "value": "business_attributes" }, { "id": 7, "type": "value", "value": "ambience_romantic" }, { "id": 4, "type": "column", "value": "attribute_value" }, { "id": 6, "type": "column", "value": "attribute_name" }, { "id": 3, "type": "column", "value": "attribute_id" }, { "id": 2, "type": "column", "value": "business_id" }, { "id": 0, "type": "table", "value": "attributes" }, { "id": 5, "type": "value", "value": "true" } ]
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[ "O", "O", "B-COLUMN", "O", "B-VALUE", "I-VALUE", "O", "O" ]
1,356
art_1
bird:test.json:1285
What is the average height and width of paintings that are oil medium in gallery 241?
SELECT avg(height_mm) , avg(width_mm) FROM paintings WHERE medium = "oil" AND LOCATION = "Gallery 241"
[ "What", "is", "the", "average", "height", "and", "width", "of", "paintings", "that", "are", "oil", "medium", "in", "gallery", "241", "?" ]
[ { "id": 6, "type": "column", "value": "Gallery 241" }, { "id": 0, "type": "table", "value": "paintings" }, { "id": 1, "type": "column", "value": "height_mm" }, { "id": 2, "type": "column", "value": "width_mm" }, { "id": 5, "type": "column", "value": "location" }, { "id": 3, "type": "column", "value": "medium" }, { "id": 4, "type": "column", "value": "oil" } ]
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[ "O", "O", "O", "O", "B-COLUMN", "O", "B-COLUMN", "O", "B-TABLE", "O", "O", "B-COLUMN", "B-COLUMN", "O", "B-COLUMN", "I-COLUMN", "O" ]
1,357
simpson_episodes
bird:train.json:4308
Write down the website address which stores the episode image of episode 5.
SELECT episode_image FROM Episode WHERE episode = 5;
[ "Write", "down", "the", "website", "address", "which", "stores", "the", "episode", "image", "of", "episode", "5", "." ]
[ { "id": 1, "type": "column", "value": "episode_image" }, { "id": 0, "type": "table", "value": "episode" }, { "id": 2, "type": "column", "value": "episode" }, { "id": 3, "type": "value", "value": "5" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "B-COLUMN", "O", "O", "B-VALUE", "O" ]
1,358
professional_basketball
bird:train.json:2919
Find the full name of the player born in Atlanta and have the highest number of blocks. Also, in which team did this player perform the most number of blocks?
SELECT T1.firstName, T1.lastName, T2.tmID FROM players AS T1 INNER JOIN players_teams AS T2 ON T1.playerID = T2.playerID WHERE T1.birthCity = 'Atlanta' ORDER BY T2.blocks DESC LIMIT 1
[ "Find", "the", "full", "name", "of", "the", "player", "born", "in", "Atlanta", "and", "have", "the", "highest", "number", "of", "blocks", ".", "Also", ",", "in", "which", "team", "did", "this", "player", "perform", "the", "most", "number", "of", "blocks", "?" ]
[ { "id": 4, "type": "table", "value": "players_teams" }, { "id": 0, "type": "column", "value": "firstname" }, { "id": 5, "type": "column", "value": "birthcity" }, { "id": 1, "type": "column", "value": "lastname" }, { "id": 8, "type": "column", "value": "playerid" }, { "id": 3, "type": "table", "value": "players" }, { "id": 6, "type": "value", "value": "Atlanta" }, { "id": 7, "type": "column", "value": "blocks" }, { "id": 2, "type": "column", "value": "tmid" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "B-VALUE", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "B-COLUMN", "O", "O", "O", "O", "O", "O", "O" ]
1,359
video_games
bird:train.json:3501
List the platform ID of the game titled Airborne Troops: Countdown to D-Day.
SELECT T1.platform_id FROM game_platform AS T1 INNER JOIN game_publisher AS T2 ON T1.game_publisher_id = T2.id INNER JOIN game AS T3 ON T2.game_id = T3.id WHERE T3.game_name = 'Airborne Troops: Countdown to D-Day'
[ "List", "the", "platform", "ID", "of", "the", "game", "titled", "Airborne", "Troops", ":", "Countdown", "to", "D", "-", "Day", "." ]
[ { "id": 3, "type": "value", "value": "Airborne Troops: Countdown to D-Day" }, { "id": 8, "type": "column", "value": "game_publisher_id" }, { "id": 5, "type": "table", "value": "game_publisher" }, { "id": 4, "type": "table", "value": "game_platform" }, { "id": 0, "type": "column", "value": "platform_id" }, { "id": 2, "type": "column", "value": "game_name" }, { "id": 6, "type": "column", "value": "game_id" }, { "id": 1, "type": "table", "value": "game" }, { "id": 7, "type": "column", "value": "id" } ]
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[ "O", "O", "B-COLUMN", "B-COLUMN", "O", "O", "B-TABLE", "O", "B-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "O" ]
1,360
food_inspection
bird:train.json:8803
What percentage of the violations for "Melody Lounge" are moderate risks?
SELECT CAST(SUM(CASE WHEN T2.risk_category = 'Moderate Risk' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(T2.business_id) FROM businesses AS T1 INNER JOIN violations AS T2 ON T1.business_id = T2.business_id WHERE T1.name = 'Melody Lounge'
[ "What", "percentage", "of", "the", "violations", "for", "\"", "Melody", "Lounge", "\"", "are", "moderate", "risks", "?" ]
[ { "id": 3, "type": "value", "value": "Melody Lounge" }, { "id": 8, "type": "column", "value": "risk_category" }, { "id": 9, "type": "value", "value": "Moderate Risk" }, { "id": 4, "type": "column", "value": "business_id" }, { "id": 0, "type": "table", "value": "businesses" }, { "id": 1, "type": "table", "value": "violations" }, { "id": 2, "type": "column", "value": "name" }, { "id": 5, "type": "value", "value": "100" }, { "id": 6, "type": "value", "value": "0" }, { "id": 7, "type": "value", "value": "1" } ]
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[ "O", "O", "O", "O", "B-TABLE", "O", "O", "B-VALUE", "I-VALUE", "O", "O", "B-VALUE", "I-VALUE", "O" ]
1,361
disney
bird:train.json:4660
How many PG adventure movies did Ron Clements direct?
SELECT COUNT(*) FROM director AS T1 INNER JOIN movies_total_gross AS T2 ON T1.name = T2.movie_title WHERE T1.director = 'Ron Clements' AND T2.MPAA_rating = 'PG' AND T2.genre = 'Adventure'
[ "How", "many", "PG", "adventure", "movies", "did", "Ron", "Clements", "direct", "?" ]
[ { "id": 1, "type": "table", "value": "movies_total_gross" }, { "id": 5, "type": "value", "value": "Ron Clements" }, { "id": 3, "type": "column", "value": "movie_title" }, { "id": 6, "type": "column", "value": "mpaa_rating" }, { "id": 9, "type": "value", "value": "Adventure" }, { "id": 0, "type": "table", "value": "director" }, { "id": 4, "type": "column", "value": "director" }, { "id": 8, "type": "column", "value": "genre" }, { "id": 2, "type": "column", "value": "name" }, { "id": 7, "type": "value", "value": "PG" } ]
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[ "O", "O", "B-VALUE", "B-COLUMN", "B-COLUMN", "O", "B-VALUE", "I-VALUE", "B-COLUMN", "O" ]
1,362
inn_1
spider:train_spider.json:2599
How many people in total can stay in the modern rooms of this inn?
SELECT sum(maxOccupancy) FROM Rooms WHERE decor = 'modern';
[ "How", "many", "people", "in", "total", "can", "stay", "in", "the", "modern", "rooms", "of", "this", "inn", "?" ]
[ { "id": 3, "type": "column", "value": "maxoccupancy" }, { "id": 2, "type": "value", "value": "modern" }, { "id": 0, "type": "table", "value": "rooms" }, { "id": 1, "type": "column", "value": "decor" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-VALUE", "B-TABLE", "O", "O", "O", "O" ]
1,363
ship_1
spider:train_spider.json:6240
Find the captain rank that has no captain in Third-rate ship of the line class.
SELECT rank FROM captain EXCEPT SELECT rank FROM captain WHERE CLASS = 'Third-rate ship of the line'
[ "Find", "the", "captain", "rank", "that", "has", "no", "captain", "in", "Third", "-", "rate", "ship", "of", "the", "line", "class", "." ]
[ { "id": 3, "type": "value", "value": "Third-rate ship of the line" }, { "id": 0, "type": "table", "value": "captain" }, { "id": 2, "type": "column", "value": "class" }, { "id": 1, "type": "column", "value": "rank" } ]
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[ "O", "O", "B-TABLE", "B-COLUMN", "O", "O", "O", "O", "O", "B-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "B-COLUMN", "O" ]
1,364
bakery_1
bird:test.json:1550
Give the ids of Cookies or Cakes that cost between 3 and 7 dollars.
SELECT id FROM goods WHERE food = "Cookie" OR food = "Cake" AND price BETWEEN 3 AND 7
[ "Give", "the", "ids", "of", "Cookies", "or", "Cakes", "that", "cost", "between", "3", "and", "7", "dollars", "." ]
[ { "id": 3, "type": "column", "value": "Cookie" }, { "id": 0, "type": "table", "value": "goods" }, { "id": 5, "type": "column", "value": "price" }, { "id": 2, "type": "column", "value": "food" }, { "id": 4, "type": "column", "value": "Cake" }, { "id": 1, "type": "column", "value": "id" }, { "id": 6, "type": "value", "value": "3" }, { "id": 7, "type": "value", "value": "7" } ]
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[ "O", "O", "B-COLUMN", "O", "B-COLUMN", "O", "B-COLUMN", "O", "O", "O", "B-VALUE", "O", "B-VALUE", "O", "O" ]
1,365
superhero
bird:dev.json:829
Which publisher created more superheroes: DC or Marvel Comics? Find the difference in the number of superheroes.
SELECT SUM(CASE WHEN T2.publisher_name = 'DC Comics' THEN 1 ELSE 0 END) - SUM(CASE WHEN T2.publisher_name = 'Marvel Comics' THEN 1 ELSE 0 END) FROM superhero AS T1 INNER JOIN publisher AS T2 ON T1.publisher_id = T2.id
[ "Which", "publisher", "created", "more", "superheroes", ":", "DC", "or", "Marvel", "Comics", "?", "Find", "the", "difference", "in", "the", "number", "of", "superheroes", "." ]
[ { "id": 6, "type": "column", "value": "publisher_name" }, { "id": 8, "type": "value", "value": "Marvel Comics" }, { "id": 2, "type": "column", "value": "publisher_id" }, { "id": 0, "type": "table", "value": "superhero" }, { "id": 1, "type": "table", "value": "publisher" }, { "id": 7, "type": "value", "value": "DC Comics" }, { "id": 3, "type": "column", "value": "id" }, { "id": 4, "type": "value", "value": "0" }, { "id": 5, "type": "value", "value": "1" } ]
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[ "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "B-VALUE", "B-VALUE", "O", "B-COLUMN", "O", "O", "O", "O", "O", "O", "B-TABLE", "O" ]
1,366
cre_Doc_Workflow
bird:test.json:2025
Show the names and descriptions for all documents.
SELECT document_name , document_description FROM Documents
[ "Show", "the", "names", "and", "descriptions", "for", "all", "documents", "." ]
[ { "id": 2, "type": "column", "value": "document_description" }, { "id": 1, "type": "column", "value": "document_name" }, { "id": 0, "type": "table", "value": "documents" } ]
[ { "entity_id": 0, "token_idxs": [ 7 ] }, { "entity_id": 1, "token_idxs": [] }, { "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", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O" ]
1,367
movies_4
bird:train.json:448
How many female crews are in the movie "Mr. Smith Goes to Washington"?
SELECT COUNT(T3.gender) FROM movie AS T1 INNER JOIN movie_cast AS T2 ON T1.movie_id = T2.movie_id INNER JOIN gender AS T3 ON T2.gender_id = T3.gender_id WHERE T1.title = 'Mr. Smith Goes to Washington' AND T3.gender = 'Female'
[ "How", "many", "female", "crews", "are", "in", "the", "movie", "\"", "Mr.", "Smith", "Goes", "to", "Washington", "\"", "?" ]
[ { "id": 6, "type": "value", "value": "Mr. Smith Goes to Washington" }, { "id": 3, "type": "table", "value": "movie_cast" }, { "id": 4, "type": "column", "value": "gender_id" }, { "id": 8, "type": "column", "value": "movie_id" }, { "id": 0, "type": "table", "value": "gender" }, { "id": 1, "type": "column", "value": "gender" }, { "id": 7, "type": "value", "value": "Female" }, { "id": 2, "type": "table", "value": "movie" }, { "id": 5, "type": "column", "value": "title" } ]
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[ "O", "O", "B-VALUE", "O", "O", "O", "O", "B-TABLE", "O", "B-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "O", "O" ]
1,368
formula_1
bird:dev.json:1008
During which lap did Lewis Hamilton take a pit stop during the 2011 Australian Grand Prix?
SELECT T1.lap FROM pitStops AS T1 INNER JOIN drivers AS T2 on T1.driverId = T2.driverId INNER JOIN races AS T3 on T1.raceId = T3.raceId WHERE T2.forename = 'Lewis' AND T2.surname = 'Hamilton' AND T3.year = 2011 AND T3.name = 'Australian Grand Prix'
[ "During", "which", "lap", "did", "Lewis", "Hamilton", "take", "a", "pit", "stop", "during", "the", "2011", "Australian", "Grand", "Prix", "?" ]
[ { "id": 12, "type": "value", "value": "Australian Grand Prix" }, { "id": 2, "type": "table", "value": "pitstops" }, { "id": 5, "type": "column", "value": "forename" }, { "id": 8, "type": "value", "value": "Hamilton" }, { "id": 13, "type": "column", "value": "driverid" }, { "id": 3, "type": "table", "value": "drivers" }, { "id": 7, "type": "column", "value": "surname" }, { "id": 4, "type": "column", "value": "raceid" }, { "id": 1, "type": "table", "value": "races" }, { "id": 6, "type": "value", "value": "Lewis" }, { "id": 9, "type": "column", "value": "year" }, { "id": 10, "type": "value", "value": "2011" }, { "id": 11, "type": "column", "value": "name" }, { "id": 0, "type": "column", "value": "lap" } ]
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[ "O", "O", "B-COLUMN", "O", "B-VALUE", "B-VALUE", "O", "O", "B-TABLE", "I-TABLE", "O", "O", "B-VALUE", "B-VALUE", "I-VALUE", "I-VALUE", "O" ]
1,369
chicago_crime
bird:train.json:8723
What is the precise coordinate of the location where simple assault incidents happened the most in Chatham?
SELECT T2.latitude, T2.longitude FROM FBI_Code AS T1 INNER JOIN Crime AS T2 ON T1.fbi_code_no = T2.fbi_code_no INNER JOIN Community_Area AS T3 ON T2.community_area_no = T3.community_area_no WHERE T1.title = 'Simple Assault' AND T3.community_area_name = 'Chatham' AND T3.community_area_no = 44 ORDER BY T2.latitude DESC, T2.longitude DESC LIMIT 1
[ "What", "is", "the", "precise", "coordinate", "of", "the", "location", "where", "simple", "assault", "incidents", "happened", "the", "most", "in", "Chatham", "?" ]
[ { "id": 8, "type": "column", "value": "community_area_name" }, { "id": 5, "type": "column", "value": "community_area_no" }, { "id": 2, "type": "table", "value": "community_area" }, { "id": 7, "type": "value", "value": "Simple Assault" }, { "id": 11, "type": "column", "value": "fbi_code_no" }, { "id": 1, "type": "column", "value": "longitude" }, { "id": 0, "type": "column", "value": "latitude" }, { "id": 3, "type": "table", "value": "fbi_code" }, { "id": 9, "type": "value", "value": "Chatham" }, { "id": 4, "type": "table", "value": "crime" }, { "id": 6, "type": "column", "value": "title" }, { "id": 10, "type": "value", "value": "44" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-VALUE", "I-VALUE", "O", "O", "O", "O", "O", "B-VALUE", "O" ]
1,370
ship_1
spider:train_spider.json:6226
Sort all captain names by their ages from old to young.
SELECT name FROM captain ORDER BY age DESC
[ "Sort", "all", "captain", "names", "by", "their", "ages", "from", "old", "to", "young", "." ]
[ { "id": 0, "type": "table", "value": "captain" }, { "id": 1, "type": "column", "value": "name" }, { "id": 2, "type": "column", "value": "age" } ]
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[ "O", "O", "B-TABLE", "B-COLUMN", "O", "O", "B-COLUMN", "O", "O", "O", "O", "O" ]
1,371
movie_2
bird:test.json:1825
Find the name of the movie that is on in both Odeon and Imperial theaters.
SELECT T1.title FROM movies AS T1 JOIN movietheaters AS T2 ON T1.code = T2.movie WHERE T2.name = 'Odeon' INTERSECT SELECT T1.title FROM movies AS T1 JOIN movietheaters AS T2 ON T1.code = T2.movie WHERE T2.name = 'Imperial'
[ "Find", "the", "name", "of", "the", "movie", "that", "is", "on", "in", "both", "Odeon", "and", "Imperial", "theaters", "." ]
[ { "id": 2, "type": "table", "value": "movietheaters" }, { "id": 5, "type": "value", "value": "Imperial" }, { "id": 1, "type": "table", "value": "movies" }, { "id": 0, "type": "column", "value": "title" }, { "id": 4, "type": "value", "value": "Odeon" }, { "id": 7, "type": "column", "value": "movie" }, { "id": 3, "type": "column", "value": "name" }, { "id": 6, "type": "column", "value": "code" } ]
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[ "O", "O", "B-COLUMN", "O", "O", "B-COLUMN", "B-TABLE", "O", "O", "O", "O", "B-COLUMN", "O", "B-VALUE", "O", "O" ]
1,372
student_assessment
spider:train_spider.json:105
List the id of students who registered course statistics in the order of registration date.
SELECT T2.student_id FROM courses AS T1 JOIN student_course_registrations AS T2 ON T1.course_id = T2.course_id WHERE T1.course_name = "statistics" ORDER BY T2.registration_date
[ "List", "the", "i", "d", "of", "students", "who", "registered", "course", "statistics", "in", "the", "order", "of", "registration", "date", "." ]
[ { "id": 2, "type": "table", "value": "student_course_registrations" }, { "id": 5, "type": "column", "value": "registration_date" }, { "id": 3, "type": "column", "value": "course_name" }, { "id": 0, "type": "column", "value": "student_id" }, { "id": 4, "type": "column", "value": "statistics" }, { "id": 6, "type": "column", "value": "course_id" }, { "id": 1, "type": "table", "value": "courses" } ]
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[ "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "B-COLUMN", "O", "B-TABLE", "I-TABLE", "I-TABLE", "B-COLUMN", "I-COLUMN", "O" ]
1,373
menu
bird:train.json:5554
What are the names of the dishes shown in the lower right corner of menu page 48706?
SELECT T2.name FROM MenuItem AS T1 INNER JOIN Dish AS T2 ON T2.id = T1.dish_id WHERE T1.xpos > 0.75 AND T1.ypos > 0.75 AND T1.menu_page_id = 48706
[ "What", "are", "the", "names", "of", "the", "dishes", "shown", "in", "the", "lower", "right", "corner", "of", "menu", "page", "48706", "?" ]
[ { "id": 8, "type": "column", "value": "menu_page_id" }, { "id": 1, "type": "table", "value": "menuitem" }, { "id": 4, "type": "column", "value": "dish_id" }, { "id": 9, "type": "value", "value": "48706" }, { "id": 0, "type": "column", "value": "name" }, { "id": 2, "type": "table", "value": "dish" }, { "id": 5, "type": "column", "value": "xpos" }, { "id": 6, "type": "value", "value": "0.75" }, { "id": 7, "type": "column", "value": "ypos" }, { "id": 3, "type": "column", "value": "id" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "B-COLUMN", "B-VALUE", "O" ]
1,374
music_2
spider:train_spider.json:5194
What is the label that has the most albums?
SELECT label FROM albums GROUP BY label ORDER BY count(*) DESC LIMIT 1
[ "What", "is", "the", "label", "that", "has", "the", "most", "albums", "?" ]
[ { "id": 0, "type": "table", "value": "albums" }, { "id": 1, "type": "column", "value": "label" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "O", "O", "O", "B-TABLE", "O" ]
1,376
books
bird:train.json:5992
List all of the books that were published in 1995.
SELECT title FROM book WHERE STRFTIME('%Y', publication_date) = '1995'
[ "List", "all", "of", "the", "books", "that", "were", "published", "in", "1995", "." ]
[ { "id": 4, "type": "column", "value": "publication_date" }, { "id": 1, "type": "column", "value": "title" }, { "id": 0, "type": "table", "value": "book" }, { "id": 2, "type": "value", "value": "1995" }, { "id": 3, "type": "value", "value": "%Y" } ]
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[ "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "O", "B-VALUE", "O" ]
1,377
manufactory_1
spider:train_spider.json:5341
What are the average prices of products, grouped by manufacturer code?
SELECT AVG(Price) , Manufacturer FROM Products GROUP BY Manufacturer
[ "What", "are", "the", "average", "prices", "of", "products", ",", "grouped", "by", "manufacturer", "code", "?" ]
[ { "id": 1, "type": "column", "value": "manufacturer" }, { "id": 0, "type": "table", "value": "products" }, { "id": 2, "type": "column", "value": "price" } ]
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[ "O", "O", "O", "O", "B-COLUMN", "O", "B-TABLE", "O", "O", "O", "B-COLUMN", "O", "O" ]
1,378
talkingdata
bird:train.json:1127
Give the number of 30-year-old users who were active in the events on 2016/5/2.
SELECT COUNT(T3.device_id) FROM app_events AS T1 INNER JOIN events AS T2 ON T1.event_id = T2.event_id INNER JOIN gender_age AS T3 ON T2.device_id = T3.device_id WHERE SUBSTR(`timestamp`, 1, 10) = '2016-05-02' AND T1.is_active = 1 AND T3.age = '30'
[ "Give", "the", "number", "of", "30", "-", "year", "-", "old", "users", "who", "were", "active", "in", "the", "events", "on", "2016/5/2", "." ]
[ { "id": 0, "type": "table", "value": "gender_age" }, { "id": 2, "type": "table", "value": "app_events" }, { "id": 4, "type": "value", "value": "2016-05-02" }, { "id": 1, "type": "column", "value": "device_id" }, { "id": 5, "type": "column", "value": "is_active" }, { "id": 10, "type": "column", "value": "timestamp" }, { "id": 9, "type": "column", "value": "event_id" }, { "id": 3, "type": "table", "value": "events" }, { "id": 7, "type": "column", "value": "age" }, { "id": 8, "type": "value", "value": "30" }, { "id": 11, "type": "value", "value": "10" }, { "id": 6, "type": "value", "value": "1" } ]
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[ "O", "O", "O", "O", "B-VALUE", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "B-VALUE", "O" ]
1,379
ship_1
spider:train_spider.json:6243
Return the name of the youngest captain.
SELECT name FROM captain ORDER BY age LIMIT 1
[ "Return", "the", "name", "of", "the", "youngest", "captain", "." ]
[ { "id": 0, "type": "table", "value": "captain" }, { "id": 1, "type": "column", "value": "name" }, { "id": 2, "type": "column", "value": "age" } ]
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[ "O", "O", "B-COLUMN", "O", "O", "O", "B-TABLE", "O" ]
1,380
student_loan
bird:train.json:4407
What is the longest students absence duration from school?
SELECT name, month FROM longest_absense_from_school WHERE `month` = ( SELECT MAX(month) FROM longest_absense_from_school )
[ "What", "is", "the", "longest", "students", "absence", "duration", "from", "school", "?" ]
[ { "id": 0, "type": "table", "value": "longest_absense_from_school" }, { "id": 2, "type": "column", "value": "month" }, { "id": 1, "type": "column", "value": "name" } ]
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[ "O", "O", "O", "O", "O", "B-TABLE", "I-TABLE", "I-TABLE", "I-TABLE", "O" ]
1,381
soccer_2016
bird:train.json:1799
What is the name of the player who won the "man of the match" award in the match on 2008/4/18?
SELECT T2.Player_Name FROM Match AS T1 INNER JOIN Player AS T2 ON T2.Player_Id = T1.Man_of_the_Match WHERE T1.Match_Date = '2008-04-18'
[ "What", "is", "the", "name", "of", "the", "player", "who", "won", "the", "\"", "man", "of", "the", "match", "\"", "award", "in", "the", "match", "on", "2008/4/18", "?" ]
[ { "id": 6, "type": "column", "value": "man_of_the_match" }, { "id": 0, "type": "column", "value": "player_name" }, { "id": 3, "type": "column", "value": "match_date" }, { "id": 4, "type": "value", "value": "2008-04-18" }, { "id": 5, "type": "column", "value": "player_id" }, { "id": 2, "type": "table", "value": "player" }, { "id": 1, "type": "table", "value": "match" } ]
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[ "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "B-TABLE", "O", "O", "O", "O", "O", "O", "B-VALUE", "O" ]
1,382
sales
bird:train.json:5468
Name the product that sold the most quantity.
SELECT T2.Name FROM Sales AS T1 INNER JOIN Products AS T2 ON T1.ProductID = T2.ProductID ORDER BY T1.Quantity DESC LIMIT 1
[ "Name", "the", "product", "that", "sold", "the", "most", "quantity", "." ]
[ { "id": 4, "type": "column", "value": "productid" }, { "id": 2, "type": "table", "value": "products" }, { "id": 3, "type": "column", "value": "quantity" }, { "id": 1, "type": "table", "value": "sales" }, { "id": 0, "type": "column", "value": "name" } ]
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[ "B-COLUMN", "O", "B-TABLE", "O", "O", "O", "O", "B-COLUMN", "O" ]
1,383
swimming
spider:train_spider.json:5620
What is the name of the stadium which held the most events?
SELECT t1.name FROM stadium AS t1 JOIN event AS t2 ON t1.id = t2.stadium_id GROUP BY t2.stadium_id ORDER BY count(*) DESC LIMIT 1
[ "What", "is", "the", "name", "of", "the", "stadium", "which", "held", "the", "most", "events", "?" ]
[ { "id": 0, "type": "column", "value": "stadium_id" }, { "id": 2, "type": "table", "value": "stadium" }, { "id": 3, "type": "table", "value": "event" }, { "id": 1, "type": "column", "value": "name" }, { "id": 4, "type": "column", "value": "id" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "O", "O", "B-TABLE", "O" ]
1,384
thrombosis_prediction
bird:dev.json:1177
Was the total cholesterol status for the patient id 2927464 on 1995-9-4 at the normal level?
SELECT CASE WHEN `T-CHO` < 250 THEN 'Normal' ELSE 'Abnormal' END FROM Laboratory WHERE ID = 2927464 AND Date = '1995-09-04'
[ "Was", "the", "total", "cholesterol", "status", "for", "the", "patient", "i", "d", "2927464", "on", "1995", "-", "9", "-", "4", "at", "the", "normal", "level", "?" ]
[ { "id": 0, "type": "table", "value": "laboratory" }, { "id": 5, "type": "value", "value": "1995-09-04" }, { "id": 1, "type": "value", "value": "Abnormal" }, { "id": 3, "type": "value", "value": "2927464" }, { "id": 6, "type": "value", "value": "Normal" }, { "id": 7, "type": "column", "value": "T-CHO" }, { "id": 4, "type": "column", "value": "date" }, { "id": 8, "type": "value", "value": "250" }, { "id": 2, "type": "column", "value": "id" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "B-VALUE", "O", "B-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "B-COLUMN", "O", "B-VALUE", "O", "O" ]
1,385
entrepreneur
spider:train_spider.json:2282
Give the total money requested by entrepreneurs who are taller than 1.85.
SELECT sum(T1.Money_Requested) FROM entrepreneur AS T1 JOIN people AS T2 ON T1.People_ID = T2.People_ID WHERE T2.Height > 1.85
[ "Give", "the", "total", "money", "requested", "by", "entrepreneurs", "who", "are", "taller", "than", "1.85", "." ]
[ { "id": 4, "type": "column", "value": "money_requested" }, { "id": 0, "type": "table", "value": "entrepreneur" }, { "id": 5, "type": "column", "value": "people_id" }, { "id": 1, "type": "table", "value": "people" }, { "id": 2, "type": "column", "value": "height" }, { "id": 3, "type": "value", "value": "1.85" } ]
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[ "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "B-TABLE", "O", "O", "O", "O", "B-VALUE", "O" ]
1,386
student_club
bird:dev.json:1353
What's Christof Nielson's zip code type?
SELECT T2.type FROM member AS T1 INNER JOIN zip_code AS T2 ON T1.zip = T2.zip_code WHERE T1.first_name = 'Christof' AND T1.last_name = 'Nielson'
[ "What", "'s", "Christof", "Nielson", "'s", "zip", "code", "type", "?" ]
[ { "id": 5, "type": "column", "value": "first_name" }, { "id": 7, "type": "column", "value": "last_name" }, { "id": 2, "type": "table", "value": "zip_code" }, { "id": 4, "type": "column", "value": "zip_code" }, { "id": 6, "type": "value", "value": "Christof" }, { "id": 8, "type": "value", "value": "Nielson" }, { "id": 1, "type": "table", "value": "member" }, { "id": 0, "type": "column", "value": "type" }, { "id": 3, "type": "column", "value": "zip" } ]
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[ "O", "O", "B-VALUE", "B-VALUE", "O", "B-COLUMN", "B-COLUMN", "B-COLUMN", "O" ]
1,387
olympics
bird:train.json:5015
Tell the host city of the 1968 Winter Olympic Games.
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 = '1968 Winter'
[ "Tell", "the", "host", "city", "of", "the", "1968", "Winter", "Olympic", "Games", "." ]
[ { "id": 3, "type": "value", "value": "1968 Winter" }, { "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", "O", "B-TABLE", "O", "O", "B-VALUE", "I-VALUE", "O", "B-TABLE", "O" ]
1,388
customers_and_addresses
spider:train_spider.json:6055
Find the name of all customers.
SELECT customer_name FROM customers
[ "Find", "the", "name", "of", "all", "customers", "." ]
[ { "id": 1, "type": "column", "value": "customer_name" }, { "id": 0, "type": "table", "value": "customers" } ]
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[ "O", "O", "O", "O", "O", "B-TABLE", "O" ]
1,390
video_games
bird:train.json:3441
Which game has sold the fewest units?
SELECT T.game_name FROM ( SELECT T1.game_name FROM game AS T1 INNER JOIN game_publisher AS T2 ON T1.id = T2.game_id INNER JOIN game_platform AS T3 ON T2.id = T3.game_publisher_id INNER JOIN region_sales AS T4 ON T3.id = T4.game_platform_id ORDER BY T4.num_sales LIMIT 1 ) t
[ "Which", "game", "has", "sold", "the", "fewest", "units", "?" ]
[ { "id": 8, "type": "column", "value": "game_publisher_id" }, { "id": 5, "type": "column", "value": "game_platform_id" }, { "id": 7, "type": "table", "value": "game_publisher" }, { "id": 3, "type": "table", "value": "game_platform" }, { "id": 1, "type": "table", "value": "region_sales" }, { "id": 0, "type": "column", "value": "game_name" }, { "id": 2, "type": "column", "value": "num_sales" }, { "id": 9, "type": "column", "value": "game_id" }, { "id": 6, "type": "table", "value": "game" }, { "id": 4, "type": "column", "value": "id" } ]
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[ "O", "B-TABLE", "O", "O", "O", "O", "O", "O" ]
1,391
movie_3
bird:train.json:9192
How many customers are from the city of Lethbridge?
SELECT COUNT(T3.customer_id) FROM city AS T1 INNER JOIN address AS T2 ON T1.city_id = T2.city_id INNER JOIN customer AS T3 ON T2.address_id = T3.address_id WHERE T1.city = 'Lethbridge'
[ "How", "many", "customers", "are", "from", "the", "city", "of", "Lethbridge", "?" ]
[ { "id": 3, "type": "column", "value": "customer_id" }, { "id": 2, "type": "value", "value": "Lethbridge" }, { "id": 6, "type": "column", "value": "address_id" }, { "id": 0, "type": "table", "value": "customer" }, { "id": 5, "type": "table", "value": "address" }, { "id": 7, "type": "column", "value": "city_id" }, { "id": 1, "type": "column", "value": "city" }, { "id": 4, "type": "table", "value": "city" } ]
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[ "O", "O", "B-TABLE", "B-TABLE", "O", "O", "B-TABLE", "O", "B-VALUE", "O" ]
1,392
assets_maintenance
spider:train_spider.json:3135
How many assets can each parts be used in? List the part name and the number.
SELECT T1.part_name , count(*) FROM Parts AS T1 JOIN Asset_Parts AS T2 ON T1.part_id = T2.part_id GROUP BY T1.part_name
[ "How", "many", "assets", "can", "each", "parts", "be", "used", "in", "?", "List", "the", "part", "name", "and", "the", "number", "." ]
[ { "id": 2, "type": "table", "value": "asset_parts" }, { "id": 0, "type": "column", "value": "part_name" }, { "id": 3, "type": "column", "value": "part_id" }, { "id": 1, "type": "table", "value": "parts" } ]
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[ "O", "O", "B-TABLE", "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "B-COLUMN", "B-COLUMN", "O", "O", "O", "O" ]
1,393
storm_record
spider:train_spider.json:2702
Show the average and maximum damage for all storms with max speed higher than 1000.
SELECT avg(damage_millions_USD) , max(damage_millions_USD) FROM storm WHERE max_speed > 1000
[ "Show", "the", "average", "and", "maximum", "damage", "for", "all", "storms", "with", "max", "speed", "higher", "than", "1000", "." ]
[ { "id": 3, "type": "column", "value": "damage_millions_usd" }, { "id": 1, "type": "column", "value": "max_speed" }, { "id": 0, "type": "table", "value": "storm" }, { "id": 2, "type": "value", "value": "1000" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "B-COLUMN", "I-COLUMN", "O", "O", "B-VALUE", "O" ]
1,394
student_loan
bird:train.json:4384
What is the average time for a disabled student to be absent from school?
SELECT AVG(T1.month) FROM longest_absense_from_school AS T1 INNER JOIN disabled AS T2 ON T1.`name` = T2.`name`
[ "What", "is", "the", "average", "time", "for", "a", "disabled", "student", "to", "be", "absent", "from", "school", "?" ]
[ { "id": 0, "type": "table", "value": "longest_absense_from_school" }, { "id": 1, "type": "table", "value": "disabled" }, { "id": 2, "type": "column", "value": "month" }, { "id": 3, "type": "column", "value": "name" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "B-TABLE", "I-TABLE", "I-TABLE", "O" ]
1,395
simpson_episodes
bird:train.json:4288
What are the roles of the cast and crew from countries other than the USA?
SELECT T2.role FROM Person AS T1 INNER JOIN Credit AS T2 ON T1.name = T2.person WHERE T1.birth_country != 'USA';
[ "What", "are", "the", "roles", "of", "the", "cast", "and", "crew", "from", "countries", "other", "than", "the", "USA", "?" ]
[ { "id": 3, "type": "column", "value": "birth_country" }, { "id": 1, "type": "table", "value": "person" }, { "id": 2, "type": "table", "value": "credit" }, { "id": 6, "type": "column", "value": "person" }, { "id": 0, "type": "column", "value": "role" }, { "id": 5, "type": "column", "value": "name" }, { "id": 4, "type": "value", "value": "USA" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "B-VALUE", "O" ]
1,396
image_and_language
bird:train.json:7580
How many images have a total of 10 attribute classes?
SELECT COUNT(IMG_ID) FROM IMG_OBJ WHERE OBJ_CLASS_ID = 10
[ "How", "many", "images", "have", "a", "total", "of", "10", "attribute", "classes", "?" ]
[ { "id": 1, "type": "column", "value": "obj_class_id" }, { "id": 0, "type": "table", "value": "img_obj" }, { "id": 3, "type": "column", "value": "img_id" }, { "id": 2, "type": "value", "value": "10" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "B-VALUE", "O", "O", "O" ]
1,397
movie_3
bird:train.json:9362
What is the title of the restricted film, whose length is 71 minutes and whose replacement cost is $29.99?
SELECT title FROM film WHERE replacement_cost = 29.99 AND rating = 'R' AND length = 71
[ "What", "is", "the", "title", "of", "the", "restricted", "film", ",", "whose", "length", "is", "71", "minutes", "and", "whose", "replacement", "cost", "is", "$", "29.99", "?" ]
[ { "id": 2, "type": "column", "value": "replacement_cost" }, { "id": 4, "type": "column", "value": "rating" }, { "id": 6, "type": "column", "value": "length" }, { "id": 1, "type": "column", "value": "title" }, { "id": 3, "type": "value", "value": "29.99" }, { "id": 0, "type": "table", "value": "film" }, { "id": 7, "type": "value", "value": "71" }, { "id": 5, "type": "value", "value": "R" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "O", "O", "B-TABLE", "O", "O", "B-COLUMN", "O", "B-VALUE", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "B-VALUE", "O" ]
1,398
chicago_crime
bird:train.json:8688
In drug abuse crimes, what percentage is related to cannabis?
SELECT CAST(COUNT(CASE WHEN T1.secondary_description LIKE '%CANNABIS%' THEN T1.secondary_description END) AS REAL) * 100 / COUNT(T1.secondary_description) FROM IUCR AS T1 INNER JOIN Crime AS T2 ON T2.iucr_no = T1.iucr_no INNER JOIN FBI_Code AS T3 ON T3.fbi_code_no = T2.fbi_code_no WHERE T3.title = 'Drug Abuse'
[ "In", "drug", "abuse", "crimes", ",", "what", "percentage", "is", "related", "to", "cannabis", "?" ]
[ { "id": 7, "type": "column", "value": "secondary_description" }, { "id": 5, "type": "column", "value": "fbi_code_no" }, { "id": 2, "type": "value", "value": "Drug Abuse" }, { "id": 9, "type": "value", "value": "%CANNABIS%" }, { "id": 0, "type": "table", "value": "fbi_code" }, { "id": 8, "type": "column", "value": "iucr_no" }, { "id": 1, "type": "column", "value": "title" }, { "id": 4, "type": "table", "value": "crime" }, { "id": 3, "type": "table", "value": "iucr" }, { "id": 6, "type": "value", "value": "100" } ]
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[ "O", "B-VALUE", "I-VALUE", "B-TABLE", "O", "O", "O", "O", "O", "O", "B-VALUE", "O" ]