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student_loan
bird:train.json:4525
List out the number of disabled students who enlisted in marines.
SELECT COUNT(T1.name) FROM disabled AS T1 INNER JOIN enlist AS T2 ON T1.name = T2.name WHERE T2.organ = 'marines'
[ "List", "out", "the", "number", "of", "disabled", "students", "who", "enlisted", "in", "marines", "." ]
[ { "id": 0, "type": "table", "value": "disabled" }, { "id": 3, "type": "value", "value": "marines" }, { "id": 1, "type": "table", "value": "enlist" }, { "id": 2, "type": "column", "value": "organ" }, { "id": 4, "type": "column", "value": "name" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "B-TABLE", "O", "O", "B-TABLE", "O", "B-VALUE", "O" ]
321
law_episode
bird:train.json:1336
List out director names that received an award along with the episode number.
SELECT T3.name, T1.episode_id FROM Episode AS T1 INNER JOIN Award AS T2 ON T1.episode_id = T2.episode_id INNER JOIN Person AS T3 ON T2.person_id = T3.person_id WHERE T2.role = 'director' AND T2.result = 'Winner'
[ "List", "out", "director", "names", "that", "received", "an", "award", "along", "with", "the", "episode", "number", "." ]
[ { "id": 1, "type": "column", "value": "episode_id" }, { "id": 5, "type": "column", "value": "person_id" }, { "id": 7, "type": "value", "value": "director" }, { "id": 3, "type": "table", "value": "episode" }, { "id": 2, "type": "table", "value": "person" }, { "id": 8, "type": "column", "value": "result" }, { "id": 9, "type": "value", "value": "Winner" }, { "id": 4, "type": "table", "value": "award" }, { "id": 0, "type": "column", "value": "name" }, { "id": 6, "type": "column", "value": "role" } ]
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[ "O", "O", "B-VALUE", "B-COLUMN", "O", "O", "O", "B-TABLE", "O", "O", "O", "B-TABLE", "O", "O" ]
322
movie_platform
bird:train.json:113
Who is the director that made the most movies? Give the director's id.
SELECT director_id FROM movies GROUP BY director_id ORDER BY COUNT(movie_id) DESC LIMIT 1
[ "Who", "is", "the", "director", "that", "made", "the", "most", "movies", "?", "Give", "the", "director", "'s", "i", "d." ]
[ { "id": 1, "type": "column", "value": "director_id" }, { "id": 2, "type": "column", "value": "movie_id" }, { "id": 0, "type": "table", "value": "movies" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "B-COLUMN", "O", "O", "O" ]
323
address
bird:train.json:5174
Provide the zip codes and their affiliated organization for the postal point under Kingsport-Bristol, TN-VA.
SELECT T2.zip_code, T2.organization FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Kingsport-Bristol, TN-VA'
[ "Provide", "the", "zip", "codes", "and", "their", "affiliated", "organization", "for", "the", "postal", "point", "under", "Kingsport", "-", "Bristol", ",", "TN", "-", "VA", "." ]
[ { "id": 5, "type": "value", "value": "Kingsport-Bristol, TN-VA" }, { "id": 1, "type": "column", "value": "organization" }, { "id": 4, "type": "column", "value": "cbsa_name" }, { "id": 0, "type": "column", "value": "zip_code" }, { "id": 3, "type": "table", "value": "zip_data" }, { "id": 2, "type": "table", "value": "cbsa" }, { "id": 6, "type": "column", "value": "cbsa" } ]
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[ "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "O", "B-COLUMN", "O", "O", "O", "O", "O", "B-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "O" ]
324
retail_world
bird:train.json:6584
Identify the total number of orders processed by Northwind employee named Andrew Fuller. What percent of those orders was shipped to Austria?
SELECT CAST(COUNT(CASE WHEN T2.ShipCountry = 'Austria' THEN 1 ELSE NULL END) AS REAL) * 100 / COUNT(T2.OrderID) FROM Employees AS T1 INNER JOIN Orders AS T2 ON T1.EmployeeID = T2.EmployeeID WHERE T1.FirstName = 'Andrew' AND T1.LastName = 'Fuller'
[ "Identify", "the", "total", "number", "of", "orders", "processed", "by", "Northwind", "employee", "named", "Andrew", "Fuller", ".", "What", "percent", "of", "those", "orders", "was", "shipped", "to", "Austria", "?" ]
[ { "id": 10, "type": "column", "value": "shipcountry" }, { "id": 2, "type": "column", "value": "employeeid" }, { "id": 0, "type": "table", "value": "employees" }, { "id": 3, "type": "column", "value": "firstname" }, { "id": 5, "type": "column", "value": "lastname" }, { "id": 8, "type": "column", "value": "orderid" }, { "id": 11, "type": "value", "value": "Austria" }, { "id": 1, "type": "table", "value": "orders" }, { "id": 4, "type": "value", "value": "Andrew" }, { "id": 6, "type": "value", "value": "Fuller" }, { "id": 7, "type": "value", "value": "100" }, { "id": 9, "type": "value", "value": "1" } ]
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[ "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "O", "B-TABLE", "B-COLUMN", "B-VALUE", "B-VALUE", "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "B-VALUE", "O" ]
325
apartment_rentals
spider:train_spider.json:1200
Show the short names of the buildings managed by "Emma".
SELECT building_short_name FROM Apartment_Buildings WHERE building_manager = "Emma"
[ "Show", "the", "short", "names", "of", "the", "buildings", "managed", "by", "\"", "Emma", "\"", "." ]
[ { "id": 0, "type": "table", "value": "apartment_buildings" }, { "id": 1, "type": "column", "value": "building_short_name" }, { "id": 2, "type": "column", "value": "building_manager" }, { "id": 3, "type": "column", "value": "Emma" } ]
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[ "O", "O", "O", "O", "O", "B-TABLE", "B-COLUMN", "I-COLUMN", "O", "O", "B-COLUMN", "O", "O" ]
326
tracking_grants_for_research
spider:train_spider.json:4342
When do all the researcher role staff start to work, and when do they stop working?
SELECT date_from , date_to FROM Project_Staff WHERE role_code = 'researcher'
[ "When", "do", "all", "the", "researcher", "role", "staff", "start", "to", "work", ",", "and", "when", "do", "they", "stop", "working", "?" ]
[ { "id": 0, "type": "table", "value": "project_staff" }, { "id": 4, "type": "value", "value": "researcher" }, { "id": 1, "type": "column", "value": "date_from" }, { "id": 3, "type": "column", "value": "role_code" }, { "id": 2, "type": "column", "value": "date_to" } ]
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[ "O", "O", "O", "O", "B-VALUE", "B-COLUMN", "B-TABLE", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O" ]
327
simpson_episodes
bird:train.json:4286
Who is the oldest among all the casts and crews?
SELECT name FROM Person ORDER BY birthdate ASC LIMIT 1;
[ "Who", "is", "the", "oldest", "among", "all", "the", "casts", "and", "crews", "?" ]
[ { "id": 2, "type": "column", "value": "birthdate" }, { "id": 0, "type": "table", "value": "person" }, { "id": 1, "type": "column", "value": "name" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O" ]
328
music_2
spider:train_spider.json:5217
What are all the instruments used by the musician with the last name "Heilo"?
SELECT instrument FROM instruments AS T1 JOIN Band AS T2 ON T1.bandmateid = T2.id WHERE T2.lastname = "Heilo"
[ "What", "are", "all", "the", "instruments", "used", "by", "the", "musician", "with", "the", "last", "name", "\"", "Heilo", "\"", "?" ]
[ { "id": 1, "type": "table", "value": "instruments" }, { "id": 0, "type": "column", "value": "instrument" }, { "id": 5, "type": "column", "value": "bandmateid" }, { "id": 3, "type": "column", "value": "lastname" }, { "id": 4, "type": "column", "value": "Heilo" }, { "id": 2, "type": "table", "value": "band" }, { "id": 6, "type": "column", "value": "id" } ]
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[ "O", "O", "O", "O", "B-COLUMN", "O", "O", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "B-COLUMN", "O", "O" ]
329
entrepreneur
spider:train_spider.json:2279
What is the name of the entrepreneur with the greatest weight?
SELECT T2.Name FROM entrepreneur AS T1 JOIN people AS T2 ON T1.People_ID = T2.People_ID ORDER BY T2.Weight DESC LIMIT 1
[ "What", "is", "the", "name", "of", "the", "entrepreneur", "with", "the", "greatest", "weight", "?" ]
[ { "id": 1, "type": "table", "value": "entrepreneur" }, { "id": 4, "type": "column", "value": "people_id" }, { "id": 2, "type": "table", "value": "people" }, { "id": 3, "type": "column", "value": "weight" }, { "id": 0, "type": "column", "value": "name" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "O", "B-COLUMN", "O" ]
330
movie_1
spider:train_spider.json:2444
What is the id of the reviewer whose name has substring “Mike”?
SELECT rID FROM Reviewer WHERE name LIKE "%Mike%"
[ "What", "is", "the", "i", "d", "of", "the", "reviewer", "whose", "name", "has", "substring", "“", "Mike", "”", "?" ]
[ { "id": 0, "type": "table", "value": "reviewer" }, { "id": 3, "type": "column", "value": "%Mike%" }, { "id": 2, "type": "column", "value": "name" }, { "id": 1, "type": "column", "value": "rid" } ]
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[ "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "B-TABLE", "O", "B-COLUMN", "O", "O", "O", "B-COLUMN", "O", "O" ]
331
cinema
spider:train_spider.json:1936
What are all the locations with a cinema?
SELECT DISTINCT LOCATION FROM cinema
[ "What", "are", "all", "the", "locations", "with", "a", "cinema", "?" ]
[ { "id": 1, "type": "column", "value": "location" }, { "id": 0, "type": "table", "value": "cinema" } ]
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[ "O", "O", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O" ]
332
mondial_geo
bird:train.json:8361
Which nation has the greatest infant mortality rate among those that attained independence in 1960?
SELECT T1.Country FROM politics AS T1 INNER JOIN population AS T2 ON T1.Country = T2.Country WHERE STRFTIME('%Y', T1.Independence) = '1960' ORDER BY T2.Infant_Mortality DESC LIMIT 1
[ "Which", "nation", "has", "the", "greatest", "infant", "mortality", "rate", "among", "those", "that", "attained", "independence", "in", "1960", "?" ]
[ { "id": 4, "type": "column", "value": "infant_mortality" }, { "id": 6, "type": "column", "value": "independence" }, { "id": 2, "type": "table", "value": "population" }, { "id": 1, "type": "table", "value": "politics" }, { "id": 0, "type": "column", "value": "country" }, { "id": 3, "type": "value", "value": "1960" }, { "id": 5, "type": "value", "value": "%Y" } ]
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[ "O", "B-TABLE", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "O", "O", "O", "B-COLUMN", "O", "B-VALUE", "O" ]
333
hospital_1
spider:train_spider.json:3932
Find the number of rooms located on each block floor.
SELECT count(*) , T1.blockfloor FROM BLOCK AS T1 JOIN room AS T2 ON T1.blockfloor = T2.blockfloor AND T1.blockcode = T2.blockcode GROUP BY T1.blockfloor
[ "Find", "the", "number", "of", "rooms", "located", "on", "each", "block", "floor", "." ]
[ { "id": 0, "type": "column", "value": "blockfloor" }, { "id": 3, "type": "column", "value": "blockcode" }, { "id": 1, "type": "table", "value": "block" }, { "id": 2, "type": "table", "value": "room" } ]
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[ "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "B-TABLE", "B-COLUMN", "O" ]
334
retail_complains
bird:train.json:380
What is the average age of clients in South Atlantic?
SELECT AVG(T1.age) FROM client AS T1 INNER JOIN district AS T2 ON T1.district_id = T2.district_id WHERE T2.division = 'South Atlantic'
[ "What", "is", "the", "average", "age", "of", "clients", "in", "South", "Atlantic", "?" ]
[ { "id": 3, "type": "value", "value": "South Atlantic" }, { "id": 5, "type": "column", "value": "district_id" }, { "id": 1, "type": "table", "value": "district" }, { "id": 2, "type": "column", "value": "division" }, { "id": 0, "type": "table", "value": "client" }, { "id": 4, "type": "column", "value": "age" } ]
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[ "O", "O", "O", "O", "B-COLUMN", "O", "B-TABLE", "O", "B-VALUE", "I-VALUE", "O" ]
335
icfp_1
spider:train_spider.json:2890
Find the papers which have "Olin Shivers" as an author.
SELECT t3.title FROM authors AS t1 JOIN authorship AS t2 ON t1.authid = t2.authid JOIN papers AS t3 ON t2.paperid = t3.paperid WHERE t1.fname = "Olin" AND t1.lname = "Shivers"
[ "Find", "the", "papers", "which", "have", "\"", "Olin", "Shivers", "\"", "as", "an", "author", "." ]
[ { "id": 3, "type": "table", "value": "authorship" }, { "id": 2, "type": "table", "value": "authors" }, { "id": 4, "type": "column", "value": "paperid" }, { "id": 8, "type": "column", "value": "Shivers" }, { "id": 1, "type": "table", "value": "papers" }, { "id": 9, "type": "column", "value": "authid" }, { "id": 0, "type": "column", "value": "title" }, { "id": 5, "type": "column", "value": "fname" }, { "id": 7, "type": "column", "value": "lname" }, { "id": 6, "type": "column", "value": "Olin" } ]
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[ "O", "O", "B-TABLE", "O", "O", "O", "B-COLUMN", "B-COLUMN", "O", "O", "O", "B-COLUMN", "O" ]
336
customers_and_addresses
spider:train_spider.json:6097
Which contact channel has been used by the customer with name "Tillman Ernser"?
SELECT DISTINCT channel_code FROM customers AS t1 JOIN customer_contact_channels AS t2 ON t1.customer_id = t2.customer_id WHERE t1.customer_name = "Tillman Ernser"
[ "Which", "contact", "channel", "has", "been", "used", "by", "the", "customer", "with", "name", "\"", "Tillman", "Ernser", "\"", "?" ]
[ { "id": 2, "type": "table", "value": "customer_contact_channels" }, { "id": 4, "type": "column", "value": "Tillman Ernser" }, { "id": 3, "type": "column", "value": "customer_name" }, { "id": 0, "type": "column", "value": "channel_code" }, { "id": 5, "type": "column", "value": "customer_id" }, { "id": 1, "type": "table", "value": "customers" } ]
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[ "O", "B-TABLE", "B-COLUMN", "O", "O", "O", "O", "O", "B-TABLE", "B-COLUMN", "I-COLUMN", "O", "B-COLUMN", "I-COLUMN", "O", "O" ]
337
voter_2
spider:train_spider.json:5504
Report all majors that have less than 3 students.
SELECT Major FROM STUDENT GROUP BY Major HAVING COUNT(*) < 3
[ "Report", "all", "majors", "that", "have", "less", "than", "3", "students", "." ]
[ { "id": 0, "type": "table", "value": "student" }, { "id": 1, "type": "column", "value": "major" }, { "id": 2, "type": "value", "value": "3" } ]
[ { "entity_id": 0, "token_idxs": [ 8 ] }, { "entity_id": 1, "token_idxs": [ 2 ] }, { "entity_id": 2, "token_idxs": [ 7 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-COLUMN", "O", "O", "O", "O", "B-VALUE", "B-TABLE", "O" ]
338
activity_1
spider:train_spider.json:6736
How many faculty members does each building have? List the result with the name of the building.
SELECT building , count(*) FROM Faculty GROUP BY building
[ "How", "many", "faculty", "members", "does", "each", "building", "have", "?", "List", "the", "result", "with", "the", "name", "of", "the", "building", "." ]
[ { "id": 1, "type": "column", "value": "building" }, { "id": 0, "type": "table", "value": "faculty" } ]
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[ "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O" ]
339
loan_1
spider:train_spider.json:3047
What is the name of the bank branch with the greatest number of customers?
SELECT bname FROM bank ORDER BY no_of_customers DESC LIMIT 1
[ "What", "is", "the", "name", "of", "the", "bank", "branch", "with", "the", "greatest", "number", "of", "customers", "?" ]
[ { "id": 2, "type": "column", "value": "no_of_customers" }, { "id": 1, "type": "column", "value": "bname" }, { "id": 0, "type": "table", "value": "bank" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O" ]
340
movie_platform
bird:train.json:135
What is the percentage of users gave "5" to the movie "Go Go Tales"?
SELECT CAST(SUM(CASE WHEN T1.rating_score = 5 THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(T1.user_id) FROM ratings AS T1 INNER JOIN movies AS T2 ON T1.movie_id = T2.movie_id WHERE T2.movie_title = 'Go Go Tales'
[ "What", "is", "the", "percentage", "of", "users", "gave", "\"", "5", "\"", "to", "the", "movie", "\"", "Go", "Go", "Tales", "\"", "?" ]
[ { "id": 9, "type": "column", "value": "rating_score" }, { "id": 2, "type": "column", "value": "movie_title" }, { "id": 3, "type": "value", "value": "Go Go Tales" }, { "id": 4, "type": "column", "value": "movie_id" }, { "id": 0, "type": "table", "value": "ratings" }, { "id": 6, "type": "column", "value": "user_id" }, { "id": 1, "type": "table", "value": "movies" }, { "id": 5, "type": "value", "value": "100" }, { "id": 7, "type": "value", "value": "0" }, { "id": 8, "type": "value", "value": "1" }, { "id": 10, "type": "value", "value": "5" } ]
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[ "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "B-VALUE", "O", "O", "O", "B-TABLE", "O", "B-VALUE", "I-VALUE", "I-VALUE", "O", "O" ]
341
movie_3
bird:train.json:9153
How many actors played a role in the 2006 film whose rental duration is 7 days, rental rate is 4.99 and is 98 minutes duration?
SELECT COUNT(T1.actor_id) FROM film_actor AS T1 INNER JOIN film AS T2 ON T1.film_id = T2.film_id WHERE T2.release_year = 2006 AND T2.rental_duration = 7 AND T2.rental_duration = 4.99 AND T2.length = 98
[ "How", "many", "actors", "played", "a", "role", "in", "the", "2006", "film", "whose", "rental", "duration", "is", "7", "days", ",", "rental", "rate", "is", "4.99", "and", "is", "98", "minutes", "duration", "?" ]
[ { "id": 6, "type": "column", "value": "rental_duration" }, { "id": 4, "type": "column", "value": "release_year" }, { "id": 0, "type": "table", "value": "film_actor" }, { "id": 2, "type": "column", "value": "actor_id" }, { "id": 3, "type": "column", "value": "film_id" }, { "id": 9, "type": "column", "value": "length" }, { "id": 1, "type": "table", "value": "film" }, { "id": 5, "type": "value", "value": "2006" }, { "id": 8, "type": "value", "value": "4.99" }, { "id": 10, "type": "value", "value": "98" }, { "id": 7, "type": "value", "value": "7" } ]
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[ "O", "O", "B-COLUMN", "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "B-VALUE", "B-TABLE", "O", "B-COLUMN", "I-COLUMN", "O", "B-VALUE", "O", "O", "O", "O", "O", "B-VALUE", "O", "O", "B-VALUE", "O", "O", "O" ]
342
bike_1
spider:train_spider.json:119
What is the id of the trip that has the shortest duration?
SELECT id FROM trip ORDER BY duration LIMIT 1
[ "What", "is", "the", "i", "d", "of", "the", "trip", "that", "has", "the", "shortest", "duration", "?" ]
[ { "id": 2, "type": "column", "value": "duration" }, { "id": 0, "type": "table", "value": "trip" }, { "id": 1, "type": "column", "value": "id" } ]
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[ "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "B-TABLE", "O", "O", "O", "O", "B-COLUMN", "O" ]
343
college_1
spider:train_spider.json:3293
Find the first name of student who is taking classes from accounting and Computer Info. Systems departments
SELECT T1.stu_fname FROM student AS T1 JOIN enroll AS T2 ON T1.stu_num = T2.stu_num JOIN CLASS AS T3 ON T2.class_code = T3.class_code JOIN course AS T4 ON T3.crs_code = T4.crs_code JOIN department AS T5 ON T5.dept_code = T4.dept_code WHERE T5.dept_name = 'Accounting' INTERSECT SELECT T1.stu_fname FROM student AS T1 JOIN enroll AS T2 ON T1.stu_num = T2.stu_num JOIN CLASS AS T3 ON T2.class_code = T3.class_code JOIN course AS T4 ON T3.crs_code = T4.crs_code JOIN department AS T5 ON T5.dept_code = T4.dept_code WHERE T5.dept_name = 'Computer Info. Systems'
[ "Find", "the", "first", "name", "of", "student", "who", "is", "taking", "classes", "from", "accounting", "and", "Computer", "Info", ".", "Systems", "departments" ]
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[ "O", "O", "B-COLUMN", "B-COLUMN", "O", "B-TABLE", "O", "O", "O", "B-TABLE", "O", "B-VALUE", "O", "B-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "B-TABLE" ]
345
race_track
spider:train_spider.json:779
Show the name of track and the number of races in each track.
SELECT T2.name , count(*) FROM race AS T1 JOIN track AS T2 ON T1.track_id = T2.track_id GROUP BY T1.track_id
[ "Show", "the", "name", "of", "track", "and", "the", "number", "of", "races", "in", "each", "track", "." ]
[ { "id": 0, "type": "column", "value": "track_id" }, { "id": 3, "type": "table", "value": "track" }, { "id": 1, "type": "column", "value": "name" }, { "id": 2, "type": "table", "value": "race" } ]
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[ "O", "O", "B-COLUMN", "O", "B-COLUMN", "O", "O", "O", "O", "B-TABLE", "O", "O", "B-TABLE", "O" ]
346
manufacturer
spider:train_spider.json:3392
Find the component amounts and names of all furnitures that have more than 10 components.
SELECT Num_of_Component , name FROM furniture WHERE Num_of_Component > 10
[ "Find", "the", "component", "amounts", "and", "names", "of", "all", "furnitures", "that", "have", "more", "than", "10", "components", "." ]
[ { "id": 1, "type": "column", "value": "num_of_component" }, { "id": 0, "type": "table", "value": "furniture" }, { "id": 2, "type": "column", "value": "name" }, { "id": 3, "type": "value", "value": "10" } ]
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[ "O", "O", "B-COLUMN", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "O", "O", "B-VALUE", "O", "O" ]
347
department_store
spider:train_spider.json:4716
What is the address for the customer with id 10?
SELECT T1.address_details FROM addresses AS T1 JOIN customer_addresses AS T2 ON T1.address_id = T2.address_id WHERE T2.customer_id = 10
[ "What", "is", "the", "address", "for", "the", "customer", "with", "i", "d", "10", "?" ]
[ { "id": 2, "type": "table", "value": "customer_addresses" }, { "id": 0, "type": "column", "value": "address_details" }, { "id": 3, "type": "column", "value": "customer_id" }, { "id": 5, "type": "column", "value": "address_id" }, { "id": 1, "type": "table", "value": "addresses" }, { "id": 4, "type": "value", "value": "10" } ]
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[ "O", "O", "O", "B-TABLE", "O", "O", "B-COLUMN", "O", "O", "O", "B-VALUE", "O" ]
348
retail_world
bird:train.json:6580
How many suppliers does Northwind have in USA?
SELECT COUNT(SupplierID) FROM Suppliers WHERE Country = 'USA'
[ "How", "many", "suppliers", "does", "Northwind", "have", "in", "USA", "?" ]
[ { "id": 3, "type": "column", "value": "supplierid" }, { "id": 0, "type": "table", "value": "suppliers" }, { "id": 1, "type": "column", "value": "country" }, { "id": 2, "type": "value", "value": "USA" } ]
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[ "O", "O", "B-TABLE", "O", "O", "O", "O", "B-VALUE", "O" ]
349
music_tracker
bird:train.json:2087
From 1980 to 2000, which artist had the most disco releases?
SELECT T1.artist FROM torrents AS T1 INNER JOIN tags AS T2 ON T1.id = T2.id WHERE T1.groupYear BETWEEN 1980 AND 2000 AND T2.tag LIKE 'disco' GROUP BY T1.artist ORDER BY COUNT(T2.tag) DESC LIMIT 1
[ "From", "1980", "to", "2000", ",", "which", "artist", "had", "the", "most", "disco", "releases", "?" ]
[ { "id": 4, "type": "column", "value": "groupyear" }, { "id": 1, "type": "table", "value": "torrents" }, { "id": 0, "type": "column", "value": "artist" }, { "id": 8, "type": "value", "value": "disco" }, { "id": 2, "type": "table", "value": "tags" }, { "id": 5, "type": "value", "value": "1980" }, { "id": 6, "type": "value", "value": "2000" }, { "id": 7, "type": "column", "value": "tag" }, { "id": 3, "type": "column", "value": "id" } ]
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[ "O", "B-VALUE", "O", "B-VALUE", "O", "O", "B-COLUMN", "O", "O", "O", "B-VALUE", "O", "O" ]
350
card_games
bird:dev.json:496
What is the number of cards are there in the set of "Rinascita di Alara"?
SELECT T1.baseSetSize FROM sets AS T1 INNER JOIN set_translations AS T2 ON T2.setCode = T1.code WHERE T2.translation = 'Rinascita di Alara'
[ "What", "is", "the", "number", "of", "cards", "are", "there", "in", "the", "set", "of", "\"", "Rinascita", "di", "Alara", "\"", "?" ]
[ { "id": 4, "type": "value", "value": "Rinascita di Alara" }, { "id": 2, "type": "table", "value": "set_translations" }, { "id": 0, "type": "column", "value": "basesetsize" }, { "id": 3, "type": "column", "value": "translation" }, { "id": 5, "type": "column", "value": "setcode" }, { "id": 1, "type": "table", "value": "sets" }, { "id": 6, "type": "column", "value": "code" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "B-COLUMN", "O", "B-VALUE", "I-VALUE", "I-VALUE", "O", "O" ]
351
works_cycles
bird:train.json:7472
What proportion of work order is in Subassembly?
SELECT 100.0 * SUM(CASE WHEN T1.Name = 'Subassembly' THEN 1 ELSE 0 END) / COUNT(T2.WorkOrderID) FROM Location AS T1 INNER JOIN WorkOrderRouting AS T2 ON T1.LocationID = T2.LocationID
[ "What", "proportion", "of", "work", "order", "is", "in", "Subassembly", "?" ]
[ { "id": 1, "type": "table", "value": "workorderrouting" }, { "id": 4, "type": "column", "value": "workorderid" }, { "id": 8, "type": "value", "value": "Subassembly" }, { "id": 2, "type": "column", "value": "locationid" }, { "id": 0, "type": "table", "value": "location" }, { "id": 3, "type": "value", "value": "100.0" }, { "id": 7, "type": "column", "value": "name" }, { "id": 5, "type": "value", "value": "0" }, { "id": 6, "type": "value", "value": "1" } ]
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[ "O", "O", "O", "B-COLUMN", "I-COLUMN", "B-TABLE", "I-TABLE", "B-VALUE", "O" ]
352
superhero
bird:dev.json:782
List the heroes' names whose eyes and hair colours are both black.
SELECT T1.superhero_name FROM superhero AS T1 INNER JOIN colour AS T2 ON T1.eye_colour_id = T2.id AND T1.hair_colour_id = T2.id WHERE T2.colour = 'Black'
[ "List", "the", "heroes", "'", "names", "whose", "eyes", "and", "hair", "colours", "are", "both", "black", "." ]
[ { "id": 0, "type": "column", "value": "superhero_name" }, { "id": 7, "type": "column", "value": "hair_colour_id" }, { "id": 5, "type": "column", "value": "eye_colour_id" }, { "id": 1, "type": "table", "value": "superhero" }, { "id": 2, "type": "table", "value": "colour" }, { "id": 3, "type": "column", "value": "colour" }, { "id": 4, "type": "value", "value": "Black" }, { "id": 6, "type": "column", "value": "id" } ]
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[ "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "O", "O", "O", "B-COLUMN", "B-COLUMN", "O", "O", "B-VALUE", "O" ]
353
public_review_platform
bird:train.json:3997
State the locations of all Pet Services business.
SELECT T1.city FROM Business AS T1 INNER JOIN Business_Categories AS T2 ON T1.business_id = T2.business_id INNER JOIN Categories AS T3 ON T2.category_id = T3.category_id WHERE T3.category_name = 'Pet Services'
[ "State", "the", "locations", "of", "all", "Pet", "Services", "business", "." ]
[ { "id": 5, "type": "table", "value": "business_categories" }, { "id": 2, "type": "column", "value": "category_name" }, { "id": 3, "type": "value", "value": "Pet Services" }, { "id": 6, "type": "column", "value": "category_id" }, { "id": 7, "type": "column", "value": "business_id" }, { "id": 1, "type": "table", "value": "categories" }, { "id": 4, "type": "table", "value": "business" }, { "id": 0, "type": "column", "value": "city" } ]
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[ "O", "O", "O", "O", "O", "B-VALUE", "I-VALUE", "B-TABLE", "O" ]
354
university
bird:train.json:8108
What is the score of university ID 68 in 2015?
SELECT score FROM university_ranking_year WHERE year = 2015 AND university_id = 68
[ "What", "is", "the", "score", "of", "university", "ID", "68", "in", "2015", "?" ]
[ { "id": 0, "type": "table", "value": "university_ranking_year" }, { "id": 4, "type": "column", "value": "university_id" }, { "id": 1, "type": "column", "value": "score" }, { "id": 2, "type": "column", "value": "year" }, { "id": 3, "type": "value", "value": "2015" }, { "id": 5, "type": "value", "value": "68" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "B-COLUMN", "I-COLUMN", "B-VALUE", "O", "B-VALUE", "O" ]
355
address
bird:train.json:5149
Based on the population in 2020, calculate the percentage for the population of Asian in the zip code where the CBSA was Atmore, AL.
SELECT CAST(T2.asian_population AS REAL) * 100 / T2.population_2010 FROM CBSA AS T1 INNER JOIN zip_data AS T2 ON T1.CBSA = T2.CBSA WHERE T1.CBSA_name = 'Atmore, AL'
[ "Based", "on", "the", "population", "in", "2020", ",", "calculate", "the", "percentage", "for", "the", "population", "of", "Asian", "in", "the", "zip", "code", "where", "the", "CBSA", "was", "Atmore", ",", "AL", "." ]
[ { "id": 7, "type": "column", "value": "asian_population" }, { "id": 4, "type": "column", "value": "population_2010" }, { "id": 3, "type": "value", "value": "Atmore, AL" }, { "id": 2, "type": "column", "value": "cbsa_name" }, { "id": 1, "type": "table", "value": "zip_data" }, { "id": 0, "type": "table", "value": "cbsa" }, { "id": 5, "type": "column", "value": "cbsa" }, { "id": 6, "type": "value", "value": "100" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "B-VALUE", "I-VALUE", "I-VALUE", "O" ]
356
movie_3
bird:train.json:9418
Please list the full names of all the customers who live in Italy.
SELECT T4.first_name, T4.last_name FROM address AS T1 INNER JOIN city AS T2 ON T1.city_id = T2.city_id INNER JOIN country AS T3 ON T2.country_id = T3.country_id INNER JOIN customer AS T4 ON T1.address_id = T4.address_id WHERE T3.country = 'Italy'
[ "Please", "list", "the", "full", "names", "of", "all", "the", "customers", "who", "live", "in", "Italy", "." ]
[ { "id": 0, "type": "column", "value": "first_name" }, { "id": 6, "type": "column", "value": "address_id" }, { "id": 9, "type": "column", "value": "country_id" }, { "id": 1, "type": "column", "value": "last_name" }, { "id": 2, "type": "table", "value": "customer" }, { "id": 3, "type": "column", "value": "country" }, { "id": 5, "type": "table", "value": "country" }, { "id": 7, "type": "table", "value": "address" }, { "id": 10, "type": "column", "value": "city_id" }, { "id": 4, "type": "value", "value": "Italy" }, { "id": 8, "type": "table", "value": "city" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "B-TABLE", "O" ]
357
race_track
spider:train_spider.json:758
What are the names, locations, and years of opening for tracks with seating higher than average?
SELECT name , LOCATION , year_opened FROM track WHERE seating > (SELECT avg(seating) FROM track)
[ "What", "are", "the", "names", ",", "locations", ",", "and", "years", "of", "opening", "for", "tracks", "with", "seating", "higher", "than", "average", "?" ]
[ { "id": 3, "type": "column", "value": "year_opened" }, { "id": 2, "type": "column", "value": "location" }, { "id": 4, "type": "column", "value": "seating" }, { "id": 0, "type": "table", "value": "track" }, { "id": 1, "type": "column", "value": "name" } ]
[ { "entity_id": 0, "token_idxs": [ 12 ] }, { "entity_id": 1, "token_idxs": [ 3 ] }, { "entity_id": 2, "token_idxs": [ 5 ] }, { "entity_id": 3, "token_idxs": [ 8, 9, 10 ] }, { "entity_id": 4, "token_idxs": [ 14 ] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-COLUMN", "O", "B-COLUMN", "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "O", "B-TABLE", "O", "B-COLUMN", "O", "O", "O", "O" ]
358
donor
bird:train.json:3288
What is the total sum of the donations paid with an optional support in projects that reach more than 300 students?
SELECT SUM(T2.dollar_amount) FROM projects AS T1 INNER JOIN donations AS T2 ON T1.projectid = T2.projectid WHERE T1.students_reached > 300 AND t2.donation_included_optional_support = 't'
[ "What", "is", "the", "total", "sum", "of", "the", "donations", "paid", "with", "an", "optional", "support", "in", "projects", "that", "reach", "more", "than", "300", "students", "?" ]
[ { "id": 6, "type": "column", "value": "donation_included_optional_support" }, { "id": 4, "type": "column", "value": "students_reached" }, { "id": 2, "type": "column", "value": "dollar_amount" }, { "id": 1, "type": "table", "value": "donations" }, { "id": 3, "type": "column", "value": "projectid" }, { "id": 0, "type": "table", "value": "projects" }, { "id": 5, "type": "value", "value": "300" }, { "id": 7, "type": "value", "value": "t" } ]
[ { "entity_id": 0, "token_idxs": [ 14 ] }, { "entity_id": 1, "token_idxs": [ 7 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [ 20 ] }, { "entity_id": 5, "token_idxs": [ 19 ] }, { "entity_id": 6, "token_idxs": [ 10, 11, 12 ] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "O", "B-TABLE", "O", "O", "O", "O", "B-VALUE", "B-COLUMN", "O" ]
359
movie_3
bird:train.json:9367
Tell me the title of the film in which Sandra Kilmer is one of the actors.
SELECT T3.title FROM film_actor AS T1 INNER JOIN actor AS T2 ON T1.actor_id = T2.actor_id INNER JOIN film AS T3 ON T1.film_id = T3.film_id WHERE T2.first_name = 'SANDRA' AND T2.last_name = 'KILMER'
[ "Tell", "me", "the", "title", "of", "the", "film", "in", "which", "Sandra", "Kilmer", "is", "one", "of", "the", "actors", "." ]
[ { "id": 2, "type": "table", "value": "film_actor" }, { "id": 5, "type": "column", "value": "first_name" }, { "id": 7, "type": "column", "value": "last_name" }, { "id": 9, "type": "column", "value": "actor_id" }, { "id": 4, "type": "column", "value": "film_id" }, { "id": 6, "type": "value", "value": "SANDRA" }, { "id": 8, "type": "value", "value": "KILMER" }, { "id": 0, "type": "column", "value": "title" }, { "id": 3, "type": "table", "value": "actor" }, { "id": 1, "type": "table", "value": "film" } ]
[ { "entity_id": 0, "token_idxs": [ 3 ] }, { "entity_id": 1, "token_idxs": [ 6 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 15 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [ 9 ] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [ 10 ] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "B-VALUE", "B-VALUE", "O", "O", "O", "O", "B-TABLE", "O" ]
360
movies_4
bird:train.json:425
Show the total number of keywords of the movie "I Hope They Serve Beer in Hell".
SELECT COUNT(T2.keyword_id) FROM movie AS T1 INNER JOIN movie_keywords AS T2 ON T1.movie_id = T2.movie_id WHERE T1.title = 'I Hope They Serve Beer in Hell'
[ "Show", "the", "total", "number", "of", "keywords", "of", "the", "movie", "\"", "I", "Hope", "They", "Serve", "Beer", "in", "Hell", "\"", "." ]
[ { "id": 3, "type": "value", "value": "I Hope They Serve Beer in Hell" }, { "id": 1, "type": "table", "value": "movie_keywords" }, { "id": 4, "type": "column", "value": "keyword_id" }, { "id": 5, "type": "column", "value": "movie_id" }, { "id": 0, "type": "table", "value": "movie" }, { "id": 2, "type": "column", "value": "title" } ]
[ { "entity_id": 0, "token_idxs": [ 8 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 2 ] }, { "entity_id": 3, "token_idxs": [ 10, 11, 12, 13, 14, 15, 16 ] }, { "entity_id": 4, "token_idxs": [ 5 ] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-COLUMN", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "B-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "O", "O" ]
361
planet_1
bird:test.json:1910
what is the total package weight for each planet, list its name ?
select t3.name , sum(t1.weight) from package as t1 join shipment as t2 on t1.shipment = t2.shipmentid join planet as t3 on t2.planet = t3.planetid group by t2.planet;
[ "what", "is", "the", "total", "package", "weight", "for", "each", "planet", ",", "list", "its", "name", "?" ]
[ { "id": 8, "type": "column", "value": "shipmentid" }, { "id": 5, "type": "table", "value": "shipment" }, { "id": 6, "type": "column", "value": "planetid" }, { "id": 7, "type": "column", "value": "shipment" }, { "id": 4, "type": "table", "value": "package" }, { "id": 0, "type": "column", "value": "planet" }, { "id": 2, "type": "table", "value": "planet" }, { "id": 3, "type": "column", "value": "weight" }, { "id": 1, "type": "column", "value": "name" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 12 ] }, { "entity_id": 2, "token_idxs": [ 8 ] }, { "entity_id": 3, "token_idxs": [ 5 ] }, { "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-TABLE", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "O", "B-COLUMN", "O" ]
362
cre_Doc_and_collections
bird:test.json:667
What is the detail of document subset with name 'Best for 2000'?
SELECT Document_Subset_Details FROM Document_Subsets WHERE Document_Subset_Name = "Best for 2000";
[ "What", "is", "the", "detail", "of", "document", "subset", "with", "name", "'", "Best", "for", "2000", "'", "?" ]
[ { "id": 1, "type": "column", "value": "document_subset_details" }, { "id": 2, "type": "column", "value": "document_subset_name" }, { "id": 0, "type": "table", "value": "document_subsets" }, { "id": 3, "type": "column", "value": "Best for 2000" } ]
[ { "entity_id": 0, "token_idxs": [ 5, 6 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 7, 8 ] }, { "entity_id": 3, "token_idxs": [ 10, 11, 12 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "B-TABLE", "I-TABLE", "B-COLUMN", "I-COLUMN", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "O", "O" ]
363
beer_factory
bird:train.json:5357
What is the precise location of the place where Tommy Kono made a purchase in 2014?
SELECT DISTINCT T1.Latitude, T1.Longitude FROM geolocation AS T1 INNER JOIN `transaction` AS T2 ON T2.LocationID = T1.LocationID INNER JOIN customers AS T3 ON T3.CustomerID = T2.CustomerID WHERE T3.First = 'Tommy' AND T3.Last = 'Kono' AND T2.TransactionDate LIKE '2014%'
[ "What", "is", "the", "precise", "location", "of", "the", "place", "where", "Tommy", "Kono", "made", "a", "purchase", "in", "2014", "?" ]
[ { "id": 10, "type": "column", "value": "transactiondate" }, { "id": 3, "type": "table", "value": "geolocation" }, { "id": 4, "type": "table", "value": "transaction" }, { "id": 5, "type": "column", "value": "customerid" }, { "id": 12, "type": "column", "value": "locationid" }, { "id": 1, "type": "column", "value": "longitude" }, { "id": 2, "type": "table", "value": "customers" }, { "id": 0, "type": "column", "value": "latitude" }, { "id": 6, "type": "column", "value": "first" }, { "id": 7, "type": "value", "value": "Tommy" }, { "id": 11, "type": "value", "value": "2014%" }, { "id": 8, "type": "column", "value": "last" }, { "id": 9, "type": "value", "value": "Kono" } ]
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[ "O", "O", "O", "O", "B-COLUMN", "O", "O", "O", "O", "B-VALUE", "B-VALUE", "O", "O", "O", "O", "B-VALUE", "O" ]
364
thrombosis_prediction
bird:dev.json:1181
For the patient who got the laboratory test of uric acid level as 8.4 on 1991-10-21, how old was he/she at that time?
SELECT STRFTIME('%Y', T2.Date) - STRFTIME('%Y', T1.Birthday) FROM Patient AS T1 INNER JOIN Laboratory AS T2 ON T1.ID = T2.ID WHERE T2.UA = 8.4 AND T2.Date = '1991-10-21'
[ "For", "the", "patient", "who", "got", "the", "laboratory", "test", "of", "uric", "acid", "level", "as", "8.4", "on", "1991", "-", "10", "-", "21", ",", "how", "old", "was", "he", "/", "she", "at", "that", "time", "?" ]
[ { "id": 1, "type": "table", "value": "laboratory" }, { "id": 6, "type": "value", "value": "1991-10-21" }, { "id": 8, "type": "column", "value": "birthday" }, { "id": 0, "type": "table", "value": "patient" }, { "id": 5, "type": "column", "value": "date" }, { "id": 4, "type": "value", "value": "8.4" }, { "id": 2, "type": "column", "value": "id" }, { "id": 3, "type": "column", "value": "ua" }, { "id": 7, "type": "value", "value": "%Y" } ]
[ { "entity_id": 0, "token_idxs": [ 2 ] }, { "entity_id": 1, "token_idxs": [ 6 ] }, { "entity_id": 2, "token_idxs": [ 10 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [ 13 ] }, { "entity_id": 5, "token_idxs": [ 27 ] }, { "entity_id": 6, "token_idxs": [ 15, 16, 17, 18, 19 ] }, { "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-TABLE", "O", "O", "O", "B-COLUMN", "O", "O", "B-VALUE", "O", "B-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "O" ]
365
movie_3
bird:train.json:9242
Among the films starred by Nick Wahlberg, what is the percentage of the films with G rating?
SELECT CAST(SUM(IIF(T3.rating = 'G', 1, 0)) AS REAL) / COUNT(T3.film_id) FROM actor AS T1 INNER JOIN film_actor AS T2 ON T1.actor_id = T2.actor_id INNER JOIN film AS T3 ON T2.film_id = T3.film_id WHERE T1.first_name = 'Elvis' AND T1.last_name = 'Marx'
[ "Among", "the", "films", "starred", "by", "Nick", "Wahlberg", ",", "what", "is", "the", "percentage", "of", "the", "films", "with", "G", "rating", "?" ]
[ { "id": 2, "type": "table", "value": "film_actor" }, { "id": 4, "type": "column", "value": "first_name" }, { "id": 6, "type": "column", "value": "last_name" }, { "id": 8, "type": "column", "value": "actor_id" }, { "id": 3, "type": "column", "value": "film_id" }, { "id": 11, "type": "column", "value": "rating" }, { "id": 1, "type": "table", "value": "actor" }, { "id": 5, "type": "value", "value": "Elvis" }, { "id": 0, "type": "table", "value": "film" }, { "id": 7, "type": "value", "value": "Marx" }, { "id": 9, "type": "value", "value": "1" }, { "id": 10, "type": "value", "value": "0" }, { "id": 12, "type": "value", "value": "G" } ]
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[ "O", "O", "B-COLUMN", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "B-VALUE", "B-COLUMN", "O" ]
366
cre_Docs_and_Epenses
spider:train_spider.json:6437
What is the project detail for the project with document "King Book"?
SELECT T1.project_details FROM Projects AS T1 JOIN Documents AS T2 ON T1.project_id = T2.project_id WHERE T2.document_name = "King Book"
[ "What", "is", "the", "project", "detail", "for", "the", "project", "with", "document", "\"", "King", "Book", "\"", "?" ]
[ { "id": 0, "type": "column", "value": "project_details" }, { "id": 3, "type": "column", "value": "document_name" }, { "id": 5, "type": "column", "value": "project_id" }, { "id": 2, "type": "table", "value": "documents" }, { "id": 4, "type": "column", "value": "King Book" }, { "id": 1, "type": "table", "value": "projects" } ]
[ { "entity_id": 0, "token_idxs": [ 3, 4 ] }, { "entity_id": 1, "token_idxs": [ 7 ] }, { "entity_id": 2, "token_idxs": [ 9 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [ 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", "O", "B-COLUMN", "I-COLUMN", "O", "O", "B-TABLE", "O", "B-TABLE", "O", "B-COLUMN", "I-COLUMN", "O", "O" ]
367
entrepreneur
spider:train_spider.json:2292
Return the investors who have invested in two or more entrepreneurs.
SELECT Investor FROM entrepreneur GROUP BY Investor HAVING COUNT(*) >= 2
[ "Return", "the", "investors", "who", "have", "invested", "in", "two", "or", "more", "entrepreneurs", "." ]
[ { "id": 0, "type": "table", "value": "entrepreneur" }, { "id": 1, "type": "column", "value": "investor" }, { "id": 2, "type": "value", "value": "2" } ]
[ { "entity_id": 0, "token_idxs": [ 10 ] }, { "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", "O", "O", "O", "O", "O", "B-TABLE", "O" ]
368
cre_Students_Information_Systems
bird:test.json:504
For each student, find the student id, student biographical data, and the number of courses he or she takes.
SELECT T1.student_id , T1.bio_data , count(*) FROM Students AS T1 JOIN Classes AS T2 ON T1.student_id = T2.student_id GROUP BY T1.student_id
[ "For", "each", "student", ",", "find", "the", "student", "i", "d", ",", "student", "biographical", "data", ",", "and", "the", "number", "of", "courses", "he", "or", "she", "takes", "." ]
[ { "id": 0, "type": "column", "value": "student_id" }, { "id": 1, "type": "column", "value": "bio_data" }, { "id": 2, "type": "table", "value": "students" }, { "id": 3, "type": "table", "value": "classes" } ]
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[ "O", "O", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "O", "B-TABLE", "O", "B-COLUMN", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O" ]
369
customers_and_orders
bird:test.json:299
What is the order status code that is most common?
SELECT order_status_code FROM Customer_orders GROUP BY order_status_code ORDER BY count(*) DESC LIMIT 1
[ "What", "is", "the", "order", "status", "code", "that", "is", "most", "common", "?" ]
[ { "id": 1, "type": "column", "value": "order_status_code" }, { "id": 0, "type": "table", "value": "customer_orders" } ]
[ { "entity_id": 0, "token_idxs": [ 1, 2, 3 ] }, { "entity_id": 1, "token_idxs": [ 4, 5 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "B-TABLE", "I-TABLE", "I-TABLE", "B-COLUMN", "I-COLUMN", "O", "O", "O", "O", "O" ]
370
e_commerce
bird:test.json:59
How many products does each gender buy?
SELECT T1.gender_code , count(*) FROM Customers AS T1 JOIN Orders AS T2 ON T1.customer_id = T2.customer_id JOIN Order_items AS T3 ON T2.order_id = T3.order_id GROUP BY T1.gender_code
[ "How", "many", "products", "does", "each", "gender", "buy", "?" ]
[ { "id": 0, "type": "column", "value": "gender_code" }, { "id": 1, "type": "table", "value": "order_items" }, { "id": 5, "type": "column", "value": "customer_id" }, { "id": 2, "type": "table", "value": "customers" }, { "id": 4, "type": "column", "value": "order_id" }, { "id": 3, "type": "table", "value": "orders" } ]
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[ "O", "O", "O", "B-TABLE", "O", "B-COLUMN", "O", "O" ]
371
coinmarketcap
bird:train.json:6266
What was the price of Bitcoin when it closed at the end of the day on 2013/4/29?
SELECT T2.close FROM coins AS T1 INNER JOIN historical AS T2 ON T1.id = T2.coin_id WHERE T2.date = '2013-04-29' AND T1.name = 'Bitcoin'
[ "What", "was", "the", "price", "of", "Bitcoin", "when", "it", "closed", "at", "the", "end", "of", "the", "day", "on", "2013/4/29", "?" ]
[ { "id": 2, "type": "table", "value": "historical" }, { "id": 6, "type": "value", "value": "2013-04-29" }, { "id": 4, "type": "column", "value": "coin_id" }, { "id": 8, "type": "value", "value": "Bitcoin" }, { "id": 0, "type": "column", "value": "close" }, { "id": 1, "type": "table", "value": "coins" }, { "id": 5, "type": "column", "value": "date" }, { "id": 7, "type": "column", "value": "name" }, { "id": 3, "type": "column", "value": "id" } ]
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[ "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "B-COLUMN", "B-COLUMN", "O", "O", "O", "O", "O", "O", "B-VALUE", "O" ]
372
retails
bird:train.json:6863
Which part has a bigger size, "pink powder drab lawn cyan" or "cornflower sky burlywood green beige"?
SELECT T.p_name FROM ( SELECT p_name, p_size FROM part WHERE p_name IN ('pink powder drab lawn cyan', 'cornflower sky burlywood green beige') ) AS T ORDER BY p_size DESC LIMIT 1
[ "Which", "part", "has", "a", "bigger", "size", ",", "\"", "pink", "powder", "drab", "lawn", "cyan", "\"", "or", "\"", "cornflower", "sky", "burlywood", "green", "beige", "\"", "?" ]
[ { "id": 4, "type": "value", "value": "cornflower sky burlywood green beige" }, { "id": 3, "type": "value", "value": "pink powder drab lawn cyan" }, { "id": 0, "type": "column", "value": "p_name" }, { "id": 1, "type": "column", "value": "p_size" }, { "id": 2, "type": "table", "value": "part" } ]
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[ "O", "B-TABLE", "O", "O", "O", "B-COLUMN", "O", "O", "B-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "O", "O", "O", "B-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "O", "O" ]
373
hockey
bird:train.json:7703
What was the number of goals did player Id "dyeba01" make in the 1921 Stanley Cup finals?
SELECT G FROM ScoringSC WHERE playerID = 'dyeba01' AND year = 1921
[ "What", "was", "the", "number", "of", "goals", "did", "player", "I", "d", "\"", "dyeba01", "\"", "make", "in", "the", "1921", "Stanley", "Cup", "finals", "?" ]
[ { "id": 0, "type": "table", "value": "scoringsc" }, { "id": 2, "type": "column", "value": "playerid" }, { "id": 3, "type": "value", "value": "dyeba01" }, { "id": 4, "type": "column", "value": "year" }, { "id": 5, "type": "value", "value": "1921" }, { "id": 1, "type": "column", "value": "g" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "B-COLUMN", "I-COLUMN", "O", "B-VALUE", "O", "O", "O", "O", "B-VALUE", "O", "O", "O", "O" ]
374
donor
bird:train.json:3214
Among the schools' projects whose donation didn't use account credits redemption,how many schools are public magnet schools?
SELECT COUNT(T1.schoolid) FROM projects AS T1 INNER JOIN donations AS T2 ON T1.projectid = T2.projectid WHERE T1.school_magnet = 't' AND T2.payment_included_acct_credit = 'f'
[ "Among", "the", "schools", "'", "projects", "whose", "donation", "did", "n't", "use", "account", "credits", "redemption", ",", "how", "many", "schools", "are", "public", "magnet", "schools", "?" ]
[ { "id": 6, "type": "column", "value": "payment_included_acct_credit" }, { "id": 4, "type": "column", "value": "school_magnet" }, { "id": 1, "type": "table", "value": "donations" }, { "id": 3, "type": "column", "value": "projectid" }, { "id": 0, "type": "table", "value": "projects" }, { "id": 2, "type": "column", "value": "schoolid" }, { "id": 5, "type": "value", "value": "t" }, { "id": 7, "type": "value", "value": "f" } ]
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[ "O", "O", "O", "O", "B-TABLE", "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "B-COLUMN", "O" ]
375
university
bird:train.json:8116
Provide the score of the most populated university in 2011.
SELECT T2.score FROM university_year AS T1 INNER JOIN university_ranking_year AS T2 ON T1.university_id = T2.university_id WHERE T1.year = 2011 ORDER BY T1.num_students DESC LIMIT 1
[ "Provide", "the", "score", "of", "the", "most", "populated", "university", "in", "2011", "." ]
[ { "id": 2, "type": "table", "value": "university_ranking_year" }, { "id": 1, "type": "table", "value": "university_year" }, { "id": 6, "type": "column", "value": "university_id" }, { "id": 5, "type": "column", "value": "num_students" }, { "id": 0, "type": "column", "value": "score" }, { "id": 3, "type": "column", "value": "year" }, { "id": 4, "type": "value", "value": "2011" } ]
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[ "O", "O", "B-COLUMN", "O", "O", "O", "O", "B-COLUMN", "B-TABLE", "B-VALUE", "O" ]
376
soccer_2016
bird:train.json:1833
Which team has the highest number of losses of all time?
SELECT T1.Team_Name FROM Team AS T1 INNER JOIN ( SELECT COUNT(Team_1) AS a, Team_1 FROM Match WHERE Team_1 <> Match_Winner GROUP BY Team_1 UNION SELECT COUNT(Team_2) AS a, Team_2 FROM Match WHERE Team_2 <> Match_Winner GROUP BY Team_2 ORDER BY a DESC LIMIT 1 ) AS T2 ON T1.Team_Id = T2.Team_1
[ "Which", "team", "has", "the", "highest", "number", "of", "losses", "of", "all", "time", "?" ]
[ { "id": 7, "type": "column", "value": "match_winner" }, { "id": 0, "type": "column", "value": "team_name" }, { "id": 2, "type": "column", "value": "team_id" }, { "id": 3, "type": "column", "value": "team_1" }, { "id": 6, "type": "column", "value": "team_2" }, { "id": 5, "type": "table", "value": "match" }, { "id": 1, "type": "table", "value": "team" }, { "id": 4, "type": "column", "value": "a" } ]
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[ "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O" ]
377
authors
bird:train.json:3658
How many papers were published in 2005. Calculate the difference between the number of paper published in 2005 and the number of paper published in the previous year.
SELECT SUM(CASE WHEN Year = 2005 THEN 1 ELSE 0 END) , SUM(CASE WHEN year = 2005 THEN 1 ELSE 0 END) - SUM(CASE WHEN year = 2004 THEN 1 ELSE 0 END) AS diff FROM Paper
[ "How", "many", "papers", "were", "published", "in", "2005", ".", "Calculate", "the", "difference", "between", "the", "number", "of", "paper", "published", "in", "2005", "and", "the", "number", "of", "paper", "published", "in", "the", "previous", "year", "." ]
[ { "id": 0, "type": "table", "value": "paper" }, { "id": 3, "type": "column", "value": "year" }, { "id": 4, "type": "value", "value": "2005" }, { "id": 5, "type": "value", "value": "2004" }, { "id": 1, "type": "value", "value": "0" }, { "id": 2, "type": "value", "value": "1" } ]
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[ "O", "O", "O", "O", "O", "O", "B-VALUE", "O", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "B-VALUE", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O" ]
378
planet_1
bird:test.json:1903
Which planet has most shipments? List the planet name.
SELECT T2.Name FROM Shipment AS T1 JOIN Planet AS T2 ON T1.Planet = T2.PlanetID GROUP BY T1.Planet ORDER BY count(*) DESC LIMIT 1;
[ "Which", "planet", "has", "most", "shipments", "?", "List", "the", "planet", "name", "." ]
[ { "id": 2, "type": "table", "value": "shipment" }, { "id": 4, "type": "column", "value": "planetid" }, { "id": 0, "type": "column", "value": "planet" }, { "id": 3, "type": "table", "value": "planet" }, { "id": 1, "type": "column", "value": "name" } ]
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[ "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "B-TABLE", "B-COLUMN", "O" ]
379
retails
bird:train.json:6887
On which date was the part "burnished seashell gainsboro navajo chocolate" in order no.1 shipped?
SELECT T1.l_shipdate FROM lineitem AS T1 INNER JOIN part AS T2 ON T1.l_partkey = T2.p_partkey WHERE T1.l_orderkey = 1 AND T2.p_name = 'burnished seashell gainsboro navajo chocolate'
[ "On", "which", "date", "was", "the", "part", "\"", "burnished", "seashell", "gainsboro", "navajo", "chocolate", "\"", "in", "order", "no.1", "shipped", "?" ]
[ { "id": 8, "type": "value", "value": "burnished seashell gainsboro navajo chocolate" }, { "id": 0, "type": "column", "value": "l_shipdate" }, { "id": 5, "type": "column", "value": "l_orderkey" }, { "id": 3, "type": "column", "value": "l_partkey" }, { "id": 4, "type": "column", "value": "p_partkey" }, { "id": 1, "type": "table", "value": "lineitem" }, { "id": 7, "type": "column", "value": "p_name" }, { "id": 2, "type": "table", "value": "part" }, { "id": 6, "type": "value", "value": "1" } ]
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[ "O", "B-COLUMN", "I-COLUMN", "O", "O", "B-TABLE", "O", "B-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "O", "O", "B-COLUMN", "O", "O", "O" ]
380
restaurant_bills
bird:test.json:628
Show the most common nationality of customers.
SELECT Nationality FROM customer GROUP BY Nationality ORDER BY COUNT(*) DESC LIMIT 1
[ "Show", "the", "most", "common", "nationality", "of", "customers", "." ]
[ { "id": 1, "type": "column", "value": "nationality" }, { "id": 0, "type": "table", "value": "customer" } ]
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[ "O", "O", "O", "O", "B-COLUMN", "O", "B-TABLE", "O" ]
381
tracking_grants_for_research
spider:train_spider.json:4382
List the organisation id with the maximum outcome count, and the count.
SELECT T1.organisation_id , count(*) FROM Projects AS T1 JOIN Project_outcomes AS T2 ON T1.project_id = T2.project_id GROUP BY T1.organisation_id ORDER BY count(*) DESC LIMIT 1
[ "List", "the", "organisation", "i", "d", "with", "the", "maximum", "outcome", "count", ",", "and", "the", "count", "." ]
[ { "id": 2, "type": "table", "value": "project_outcomes" }, { "id": 0, "type": "column", "value": "organisation_id" }, { "id": 3, "type": "column", "value": "project_id" }, { "id": 1, "type": "table", "value": "projects" } ]
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[ "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "O", "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "O" ]
382
toxicology
bird:dev.json:223
What are the atom IDs of the bond TR000_2_5?
SELECT T.atom_id FROM connected AS T WHERE T.bond_id = 'TR000_2_5'
[ "What", "are", "the", "atom", "IDs", "of", "the", "bond", "TR000_2_5", "?" ]
[ { "id": 0, "type": "table", "value": "connected" }, { "id": 3, "type": "value", "value": "TR000_2_5" }, { "id": 1, "type": "column", "value": "atom_id" }, { "id": 2, "type": "column", "value": "bond_id" } ]
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[ "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "B-COLUMN", "B-VALUE", "O" ]
383
school_finance
spider:train_spider.json:1897
Show the names of donors who donated to both school "Glenn" and "Triton."
SELECT T1.donator_name FROM endowment AS T1 JOIN school AS T2 ON T1.school_id = T2.school_id WHERE T2.school_name = 'Glenn' INTERSECT SELECT T1.donator_name FROM endowment AS T1 JOIN school AS T2 ON T1.school_id = T2.school_id WHERE T2.school_name = 'Triton'
[ "Show", "the", "names", "of", "donors", "who", "donated", "to", "both", "school", "\"", "Glenn", "\"", "and", "\"", "Triton", ".", "\"" ]
[ { "id": 0, "type": "column", "value": "donator_name" }, { "id": 3, "type": "column", "value": "school_name" }, { "id": 1, "type": "table", "value": "endowment" }, { "id": 6, "type": "column", "value": "school_id" }, { "id": 2, "type": "table", "value": "school" }, { "id": 5, "type": "value", "value": "Triton" }, { "id": 4, "type": "value", "value": "Glenn" } ]
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[ "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "B-VALUE", "O", "O", "O", "B-VALUE", "O", "O" ]
384
hockey
bird:train.json:7656
Please list the Nicknames of the players who got in the Hall of Fame in 2007.
SELECT DISTINCT T1.nameNick FROM Master AS T1 INNER JOIN HOF AS T2 ON T1.hofID = T2.hofID WHERE T2.year = 2007
[ "Please", "list", "the", "Nicknames", "of", "the", "players", "who", "got", "in", "the", "Hall", "of", "Fame", "in", "2007", "." ]
[ { "id": 0, "type": "column", "value": "namenick" }, { "id": 1, "type": "table", "value": "master" }, { "id": 5, "type": "column", "value": "hofid" }, { "id": 3, "type": "column", "value": "year" }, { "id": 4, "type": "value", "value": "2007" }, { "id": 2, "type": "table", "value": "hof" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "B-VALUE", "O" ]
385
mountain_photos
spider:train_spider.json:3711
How many camera lenses have a focal length longer than 15 mm?
SELECT count(*) FROM camera_lens WHERE focal_length_mm > 15
[ "How", "many", "camera", "lenses", "have", "a", "focal", "length", "longer", "than", "15", "mm", "?" ]
[ { "id": 1, "type": "column", "value": "focal_length_mm" }, { "id": 0, "type": "table", "value": "camera_lens" }, { "id": 2, "type": "value", "value": "15" } ]
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[ "O", "O", "B-TABLE", "I-TABLE", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "B-VALUE", "O", "O" ]
386
art_1
bird:test.json:1289
What are largest height and width dimensions for paintings in each year?
SELECT max(height_mm) , max(width_mm) , YEAR FROM paintings GROUP BY YEAR ORDER BY YEAR
[ "What", "are", "largest", "height", "and", "width", "dimensions", "for", "paintings", "in", "each", "year", "?" ]
[ { "id": 0, "type": "table", "value": "paintings" }, { "id": 2, "type": "column", "value": "height_mm" }, { "id": 3, "type": "column", "value": "width_mm" }, { "id": 1, "type": "column", "value": "year" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "B-COLUMN", "O" ]
388
codebase_comments
bird:train.json:653
What the percentage of the english methods among the methods whose comments is XML format?
SELECT CAST(SUM(CASE WHEN Lang = 'en' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(Name) FROM Method WHERE CommentIsXml = 1
[ "What", "the", "percentage", "of", "the", "english", "methods", "among", "the", "methods", "whose", "comments", "is", "XML", "format", "?" ]
[ { "id": 1, "type": "column", "value": "commentisxml" }, { "id": 0, "type": "table", "value": "method" }, { "id": 4, "type": "column", "value": "name" }, { "id": 6, "type": "column", "value": "lang" }, { "id": 3, "type": "value", "value": "100" }, { "id": 7, "type": "value", "value": "en" }, { "id": 2, "type": "value", "value": "1" }, { "id": 5, "type": "value", "value": "0" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "B-TABLE", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "O", "O" ]
389
sales_in_weather
bird:train.json:8147
How many units of item no.5 were sold in store no.3 on the day the temperature range was the biggest?
SELECT t2.units FROM relation AS T1 INNER JOIN sales_in_weather AS T2 ON T1.store_nbr = T2.store_nbr INNER JOIN weather AS T3 ON T1.station_nbr = T3.station_nbr WHERE T2.store_nbr = 3 AND T2.item_nbr = 5 ORDER BY t3.tmax - t3.tmin DESC LIMIT 1
[ "How", "many", "units", "of", "item", "no.5", "were", "sold", "in", "store", "no.3", "on", "the", "day", "the", "temperature", "range", "was", "the", "biggest", "?" ]
[ { "id": 3, "type": "table", "value": "sales_in_weather" }, { "id": 4, "type": "column", "value": "station_nbr" }, { "id": 5, "type": "column", "value": "store_nbr" }, { "id": 2, "type": "table", "value": "relation" }, { "id": 7, "type": "column", "value": "item_nbr" }, { "id": 1, "type": "table", "value": "weather" }, { "id": 0, "type": "column", "value": "units" }, { "id": 9, "type": "column", "value": "tmax" }, { "id": 10, "type": "column", "value": "tmin" }, { "id": 6, "type": "value", "value": "3" }, { "id": 8, "type": "value", "value": "5" } ]
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[ "O", "O", "B-COLUMN", "O", "B-COLUMN", "O", "O", "O", "B-COLUMN", "B-COLUMN", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "I-TABLE", "O", "O" ]
390
university
bird:train.json:8008
Give the year where a university had the lowest number of students.
SELECT year FROM university_year ORDER BY num_students ASC LIMIT 1
[ "Give", "the", "year", "where", "a", "university", "had", "the", "lowest", "number", "of", "students", "." ]
[ { "id": 0, "type": "table", "value": "university_year" }, { "id": 2, "type": "column", "value": "num_students" }, { "id": 1, "type": "column", "value": "year" } ]
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[ "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "B-COLUMN", "O" ]
391
donor
bird:train.json:3234
Is the donor of the project 'Calculate, Financial Security For Tomorrow Starts Today! ' a teacher?
SELECT T2.is_teacher_acct FROM essays AS T1 INNER JOIN donations AS T2 ON T1.projectid = T2.projectid WHERE T1.title LIKE 'Calculate, Financial Security For Tomorrow Starts Today! '
[ "Is", "the", "donor", "of", "the", "project", "'", "Calculate", ",", "Financial", "Security", "For", "Tomorrow", "Starts", "Today", "!", "'", "a", "teacher", "?" ]
[ { "id": 4, "type": "value", "value": "Calculate, Financial Security For Tomorrow Starts Today! " }, { "id": 0, "type": "column", "value": "is_teacher_acct" }, { "id": 2, "type": "table", "value": "donations" }, { "id": 5, "type": "column", "value": "projectid" }, { "id": 1, "type": "table", "value": "essays" }, { "id": 3, "type": "column", "value": "title" } ]
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[ "O", "O", "O", "O", "O", "B-COLUMN", "O", "B-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "O", "B-COLUMN", "O" ]
392
college_2
spider:train_spider.json:1333
Give the name of the student in the History department with the most credits.
SELECT name FROM student WHERE dept_name = 'History' ORDER BY tot_cred DESC LIMIT 1
[ "Give", "the", "name", "of", "the", "student", "in", "the", "History", "department", "with", "the", "most", "credits", "." ]
[ { "id": 2, "type": "column", "value": "dept_name" }, { "id": 4, "type": "column", "value": "tot_cred" }, { "id": 0, "type": "table", "value": "student" }, { "id": 3, "type": "value", "value": "History" }, { "id": 1, "type": "column", "value": "name" } ]
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[ "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "B-VALUE", "B-COLUMN", "O", "O", "B-COLUMN", "I-COLUMN", "O" ]
393
regional_sales
bird:train.json:2726
Among the products with an order quantity of no less than 5 that was shipped in the month of May 2019, what is the name of the product with the lowest net profit?
SELECT T2.`Product Name` FROM `Sales Orders` AS T1 INNER JOIN Products AS T2 ON T2.ProductID = T1._ProductID WHERE T1.`Order Quantity` > 5 AND ShipDate LIKE '5/%/19' ORDER BY REPLACE(T1.`Unit Price`, ',', '') - REPLACE(T1.`Unit Cost`, ',', '') ASC LIMIT 1
[ "Among", "the", "products", "with", "an", "order", "quantity", "of", "no", "less", "than", "5", "that", "was", "shipped", "in", "the", "month", "of", "May", "2019", ",", "what", "is", "the", "name", "of", "the", "product", "with", "the", "lowest", "net", "profit", "?" ]
[ { "id": 5, "type": "column", "value": "Order Quantity" }, { "id": 0, "type": "column", "value": "Product Name" }, { "id": 1, "type": "table", "value": "Sales Orders" }, { "id": 4, "type": "column", "value": "_productid" }, { "id": 9, "type": "column", "value": "Unit Price" }, { "id": 3, "type": "column", "value": "productid" }, { "id": 11, "type": "column", "value": "Unit Cost" }, { "id": 2, "type": "table", "value": "products" }, { "id": 7, "type": "column", "value": "shipdate" }, { "id": 8, "type": "value", "value": "5/%/19" }, { "id": 6, "type": "value", "value": "5" }, { "id": 10, "type": "value", "value": "," } ]
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[ "O", "O", "B-TABLE", "O", "B-TABLE", "I-TABLE", "B-COLUMN", "I-COLUMN", "O", "O", "O", "B-VALUE", "O", "O", "B-COLUMN", "O", "O", "O", "O", "O", "O", "B-VALUE", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O" ]
394
talkingdata
bird:train.json:1113
List at least 3 categories with the lowest number of users.
SELECT T1.category FROM label_categories AS T1 INNER JOIN app_labels AS T2 ON T1.label_id = T2.label_id ORDER BY T2.label_id LIMIT 3
[ "List", "at", "least", "3", "categories", "with", "the", "lowest", "number", "of", "users", "." ]
[ { "id": 1, "type": "table", "value": "label_categories" }, { "id": 2, "type": "table", "value": "app_labels" }, { "id": 0, "type": "column", "value": "category" }, { "id": 3, "type": "column", "value": "label_id" } ]
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[ "O", "O", "O", "O", "B-COLUMN", "O", "O", "O", "O", "O", "O", "O" ]
395
performance_attendance
spider:train_spider.json:1307
How many performances are there?
SELECT count(*) FROM performance
[ "How", "many", "performances", "are", "there", "?" ]
[ { "id": 0, "type": "table", "value": "performance" } ]
[ { "entity_id": 0, "token_idxs": [ 2 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-TABLE", "O", "O", "O" ]
396
musical
spider:train_spider.json:269
What are the names of musicals who have no actors?
SELECT Name FROM musical WHERE Musical_ID NOT IN (SELECT Musical_ID FROM actor)
[ "What", "are", "the", "names", "of", "musicals", "who", "have", "no", "actors", "?" ]
[ { "id": 2, "type": "column", "value": "musical_id" }, { "id": 0, "type": "table", "value": "musical" }, { "id": 3, "type": "table", "value": "actor" }, { "id": 1, "type": "column", "value": "name" } ]
[ { "entity_id": 0, "token_idxs": [ 5 ] }, { "entity_id": 1, "token_idxs": [ 3 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 9 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-COLUMN", "O", "B-TABLE", "O", "O", "O", "B-TABLE", "O" ]
397
retail_complains
bird:train.json:373
List the full names and phone numbers of clients that were from the Pacific.
SELECT T1.first, T1.middle, T1.last, T1.phone FROM client AS T1 INNER JOIN district AS T2 ON T1.district_id = T2.district_id WHERE T2.division = 'Pacific'
[ "List", "the", "full", "names", "and", "phone", "numbers", "of", "clients", "that", "were", "from", "the", "Pacific", "." ]
[ { "id": 8, "type": "column", "value": "district_id" }, { "id": 5, "type": "table", "value": "district" }, { "id": 6, "type": "column", "value": "division" }, { "id": 7, "type": "value", "value": "Pacific" }, { "id": 1, "type": "column", "value": "middle" }, { "id": 4, "type": "table", "value": "client" }, { "id": 0, "type": "column", "value": "first" }, { "id": 3, "type": "column", "value": "phone" }, { "id": 2, "type": "column", "value": "last" } ]
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[ "B-COLUMN", "O", "O", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "O", "O", "B-VALUE", "O" ]
398
world_development_indicators
bird:train.json:2099
What is the description of the footnote on the series code AG.LND.FRST.K2 in 1990 for Aruba?
SELECT T2.Description FROM Country AS T1 INNER JOIN FootNotes AS T2 ON T1.CountryCode = T2.Countrycode WHERE T1.ShortName = 'Aruba' AND T2.Seriescode = 'AG.LND.FRST.K2' AND T2.Year = 'YR1990'
[ "What", "is", "the", "description", "of", "the", "footnote", "on", "the", "series", "code", "AG.LND.FRST.K2", "in", "1990", "for", "Aruba", "?" ]
[ { "id": 7, "type": "value", "value": "AG.LND.FRST.K2" }, { "id": 0, "type": "column", "value": "description" }, { "id": 3, "type": "column", "value": "countrycode" }, { "id": 6, "type": "column", "value": "seriescode" }, { "id": 2, "type": "table", "value": "footnotes" }, { "id": 4, "type": "column", "value": "shortname" }, { "id": 1, "type": "table", "value": "country" }, { "id": 9, "type": "value", "value": "YR1990" }, { "id": 5, "type": "value", "value": "Aruba" }, { "id": 8, "type": "column", "value": "year" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "B-COLUMN", "I-COLUMN", "B-VALUE", "O", "B-VALUE", "O", "B-VALUE", "O" ]
399
customers_and_products_contacts
spider:train_spider.json:5662
Show the minimum, maximum, average price for all products.
SELECT min(product_price) , max(product_price) , avg(product_price) FROM products
[ "Show", "the", "minimum", ",", "maximum", ",", "average", "price", "for", "all", "products", "." ]
[ { "id": 1, "type": "column", "value": "product_price" }, { "id": 0, "type": "table", "value": "products" } ]
[ { "entity_id": 0, "token_idxs": [ 10 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "O" ]
401
shipping
bird:train.json:5584
How many shipments in 2017 were done by Sue Newell?
SELECT COUNT(*) FROM shipment AS T1 INNER JOIN driver AS T2 ON T1.driver_id = T2.driver_id WHERE STRFTIME('%Y', T1.ship_date) = '2017' AND T2.first_name = 'Sue' AND T2.last_name = 'Newell'
[ "How", "many", "shipments", "in", "2017", "were", "done", "by", "Sue", "Newell", "?" ]
[ { "id": 4, "type": "column", "value": "first_name" }, { "id": 2, "type": "column", "value": "driver_id" }, { "id": 6, "type": "column", "value": "last_name" }, { "id": 9, "type": "column", "value": "ship_date" }, { "id": 0, "type": "table", "value": "shipment" }, { "id": 1, "type": "table", "value": "driver" }, { "id": 7, "type": "value", "value": "Newell" }, { "id": 3, "type": "value", "value": "2017" }, { "id": 5, "type": "value", "value": "Sue" }, { "id": 8, "type": "value", "value": "%Y" } ]
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[ "O", "O", "B-TABLE", "O", "B-VALUE", "O", "O", "O", "B-VALUE", "B-VALUE", "O" ]
402
law_episode
bird:train.json:1275
Who is the tallest camera operator?
SELECT T2.name FROM Credit AS T1 INNER JOIN Person AS T2 ON T2.person_id = T1.person_id WHERE T1.role = 'camera operator' ORDER BY T2.height_meters DESC LIMIT 1
[ "Who", "is", "the", "tallest", "camera", "operator", "?" ]
[ { "id": 4, "type": "value", "value": "camera operator" }, { "id": 5, "type": "column", "value": "height_meters" }, { "id": 6, "type": "column", "value": "person_id" }, { "id": 1, "type": "table", "value": "credit" }, { "id": 2, "type": "table", "value": "person" }, { "id": 0, "type": "column", "value": "name" }, { "id": 3, "type": "column", "value": "role" } ]
[ { "entity_id": 0, "token_idxs": [ 4 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [ 5 ] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "B-COLUMN", "B-VALUE", "O" ]
403
perpetrator
spider:train_spider.json:2313
What are the names of perpetrators in country "China" or "Japan"?
SELECT T1.Name FROM people AS T1 JOIN perpetrator AS T2 ON T1.People_ID = T2.People_ID WHERE T2.Country = "China" OR T2.Country = "Japan"
[ "What", "are", "the", "names", "of", "perpetrators", "in", "country", "\"", "China", "\"", "or", "\"", "Japan", "\"", "?" ]
[ { "id": 2, "type": "table", "value": "perpetrator" }, { "id": 3, "type": "column", "value": "people_id" }, { "id": 4, "type": "column", "value": "country" }, { "id": 1, "type": "table", "value": "people" }, { "id": 5, "type": "column", "value": "China" }, { "id": 6, "type": "column", "value": "Japan" }, { "id": 0, "type": "column", "value": "name" } ]
[ { "entity_id": 0, "token_idxs": [ 3 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 5 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [ 7 ] }, { "entity_id": 5, "token_idxs": [ 9 ] }, { "entity_id": 6, "token_idxs": [ 13 ] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-COLUMN", "O", "B-TABLE", "O", "B-COLUMN", "O", "B-COLUMN", "O", "O", "O", "B-COLUMN", "O", "O" ]
404
simpson_episodes
bird:train.json:4304
What is the average height of people from USA?
SELECT AVG(height_meters) FROM Person WHERE birth_country = 'USA';
[ "What", "is", "the", "average", "height", "of", "people", "from", "USA", "?" ]
[ { "id": 1, "type": "column", "value": "birth_country" }, { "id": 3, "type": "column", "value": "height_meters" }, { "id": 0, "type": "table", "value": "person" }, { "id": 2, "type": "value", "value": "USA" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 8 ] }, { "entity_id": 3, "token_idxs": [ 4 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "B-COLUMN", "O", "O", "O", "B-VALUE", "O" ]
405
card_games
bird:dev.json:435
How many card border with black color ? List out the card id.
SELECT id FROM cards WHERE borderColor = 'black' GROUP BY id
[ "How", "many", "card", "border", "with", "black", "color", "?", "List", "out", "the", "card", "i", "d." ]
[ { "id": 2, "type": "column", "value": "bordercolor" }, { "id": 0, "type": "table", "value": "cards" }, { "id": 3, "type": "value", "value": "black" }, { "id": 1, "type": "column", "value": "id" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "B-VALUE", "O", "O", "O", "O", "O", "B-TABLE", "B-COLUMN", "I-COLUMN" ]
407
financial
bird:dev.json:113
For the client who applied 98832 USD loan in 1996/1/3, when was his/her birthday?
SELECT T4.birth_date FROM loan AS T1 INNER JOIN account AS T2 ON T1.account_id = T2.account_id INNER JOIN disp AS T3 ON T2.account_id = T3.account_id INNER JOIN client AS T4 ON T3.client_id = T4.client_id WHERE T1.date = '1996-01-03' AND T1.amount = 98832
[ "For", "the", "client", "who", "applied", "98832", "USD", "loan", "in", "1996/1/3", ",", "when", "was", "his", "/", "her", "birthday", "?" ]
[ { "id": 0, "type": "column", "value": "birth_date" }, { "id": 5, "type": "value", "value": "1996-01-03" }, { "id": 10, "type": "column", "value": "account_id" }, { "id": 3, "type": "column", "value": "client_id" }, { "id": 9, "type": "table", "value": "account" }, { "id": 1, "type": "table", "value": "client" }, { "id": 6, "type": "column", "value": "amount" }, { "id": 7, "type": "value", "value": "98832" }, { "id": 2, "type": "table", "value": "disp" }, { "id": 4, "type": "column", "value": "date" }, { "id": 8, "type": "table", "value": "loan" } ]
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[ "O", "O", "B-TABLE", "O", "O", "B-VALUE", "O", "B-TABLE", "O", "B-VALUE", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O" ]
408
world_development_indicators
bird:train.json:2132
What is the minimum of International migrant stock, total of heavily indebted poor countries?
SELECT MIN(T2.Value) FROM Country AS T1 INNER JOIN Indicators AS T2 ON T1.CountryCode = T2.CountryCode WHERE T1.OtherGroups = 'HIPC' AND T2.IndicatorName = 'International migrant stock, total'
[ "What", "is", "the", "minimum", "of", "International", "migrant", "stock", ",", "total", "of", "heavily", "indebted", "poor", "countries", "?" ]
[ { "id": 7, "type": "value", "value": "International migrant stock, total" }, { "id": 6, "type": "column", "value": "indicatorname" }, { "id": 3, "type": "column", "value": "countrycode" }, { "id": 4, "type": "column", "value": "othergroups" }, { "id": 1, "type": "table", "value": "indicators" }, { "id": 0, "type": "table", "value": "country" }, { "id": 2, "type": "column", "value": "value" }, { "id": 5, "type": "value", "value": "HIPC" } ]
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[ "O", "O", "O", "O", "O", "B-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "O", "O", "O", "O", "B-TABLE", "O" ]
410
station_weather
spider:train_spider.json:3158
show all train numbers and names ordered by their time from early to late.
SELECT train_number , name FROM train ORDER BY TIME
[ "show", "all", "train", "numbers", "and", "names", "ordered", "by", "their", "time", "from", "early", "to", "late", "." ]
[ { "id": 1, "type": "column", "value": "train_number" }, { "id": 0, "type": "table", "value": "train" }, { "id": 2, "type": "column", "value": "name" }, { "id": 3, "type": "column", "value": "time" } ]
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[ "O", "O", "B-TABLE", "B-COLUMN", "O", "B-COLUMN", "O", "O", "O", "B-COLUMN", "O", "O", "O", "O", "O" ]
411
behavior_monitoring
spider:train_spider.json:3111
What are the code and description of the least frequent detention type ?
SELECT T1.detention_type_code , T2.detention_type_description FROM Detention AS T1 JOIN Ref_Detention_Type AS T2 ON T1.detention_type_code = T2.detention_type_code GROUP BY T1.detention_type_code ORDER BY count(*) ASC LIMIT 1
[ "What", "are", "the", "code", "and", "description", "of", "the", "least", "frequent", "detention", "type", "?" ]
[ { "id": 1, "type": "column", "value": "detention_type_description" }, { "id": 0, "type": "column", "value": "detention_type_code" }, { "id": 3, "type": "table", "value": "ref_detention_type" }, { "id": 2, "type": "table", "value": "detention" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "B-TABLE", "O" ]
412
flight_4
spider:train_spider.json:6835
Find the names of the top 10 airlines that operate the most number of routes.
SELECT T1.name , T2.alid FROM airlines AS T1 JOIN routes AS T2 ON T1.alid = T2.alid GROUP BY T2.alid ORDER BY count(*) DESC LIMIT 10
[ "Find", "the", "names", "of", "the", "top", "10", "airlines", "that", "operate", "the", "most", "number", "of", "routes", "." ]
[ { "id": 2, "type": "table", "value": "airlines" }, { "id": 3, "type": "table", "value": "routes" }, { "id": 0, "type": "column", "value": "alid" }, { "id": 1, "type": "column", "value": "name" } ]
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[ "O", "O", "B-COLUMN", "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "B-TABLE", "O" ]
413
activity_1
spider:train_spider.json:6725
Show ids for all the male faculty.
SELECT FacID FROM Faculty WHERE Sex = 'M'
[ "Show", "ids", "for", "all", "the", "male", "faculty", "." ]
[ { "id": 0, "type": "table", "value": "faculty" }, { "id": 1, "type": "column", "value": "facid" }, { "id": 2, "type": "column", "value": "sex" }, { "id": 3, "type": "value", "value": "M" } ]
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[ "O", "O", "O", "O", "O", "O", "B-TABLE", "O" ]
414
food_inspection
bird:train.json:8846
List the tax code and inspection type of the business named "Rue Lepic".
SELECT DISTINCT T3.tax_code, T2.type FROM violations AS T1 INNER JOIN inspections AS T2 ON T1.business_id = T2.business_id INNER JOIN businesses AS T3 ON T2.business_id = T3.business_id WHERE T3.name = 'Rue Lepic'
[ "List", "the", "tax", "code", "and", "inspection", "type", "of", "the", "business", "named", "\"", "Rue", "Lepic", "\"", "." ]
[ { "id": 6, "type": "table", "value": "inspections" }, { "id": 7, "type": "column", "value": "business_id" }, { "id": 2, "type": "table", "value": "businesses" }, { "id": 5, "type": "table", "value": "violations" }, { "id": 4, "type": "value", "value": "Rue Lepic" }, { "id": 0, "type": "column", "value": "tax_code" }, { "id": 1, "type": "column", "value": "type" }, { "id": 3, "type": "column", "value": "name" } ]
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[ "O", "O", "B-COLUMN", "I-COLUMN", "O", "B-TABLE", "B-COLUMN", "O", "O", "B-TABLE", "B-COLUMN", "O", "B-VALUE", "I-VALUE", "O", "O" ]
415
works_cycles
bird:train.json:7049
Please list the unit measure code of the component that is of the greatest need in quantity to create the assembly.
SELECT UnitMeasureCode FROM BillOfMaterials ORDER BY PerAssemblyQty DESC LIMIT 1
[ "Please", "list", "the", "unit", "measure", "code", "of", "the", "component", "that", "is", "of", "the", "greatest", "need", "in", "quantity", "to", "create", "the", "assembly", "." ]
[ { "id": 0, "type": "table", "value": "billofmaterials" }, { "id": 1, "type": "column", "value": "unitmeasurecode" }, { "id": 2, "type": "column", "value": "perassemblyqty" } ]
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[ "O", "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O" ]
416
student_club
bird:dev.json:1459
What is the major of Garrett Gerke and which department does it belong to?
SELECT T2.major_name, T2.department FROM member AS T1 INNER JOIN major AS T2 ON T2.major_id = T1.link_to_major WHERE T1.first_name = 'Garrett' AND T1.last_name = 'Gerke'
[ "What", "is", "the", "major", "of", "Garrett", "Gerke", "and", "which", "department", "does", "it", "belong", "to", "?" ]
[ { "id": 5, "type": "column", "value": "link_to_major" }, { "id": 0, "type": "column", "value": "major_name" }, { "id": 1, "type": "column", "value": "department" }, { "id": 6, "type": "column", "value": "first_name" }, { "id": 8, "type": "column", "value": "last_name" }, { "id": 4, "type": "column", "value": "major_id" }, { "id": 7, "type": "value", "value": "Garrett" }, { "id": 2, "type": "table", "value": "member" }, { "id": 3, "type": "table", "value": "major" }, { "id": 9, "type": "value", "value": "Gerke" } ]
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[ "O", "O", "O", "B-TABLE", "O", "B-VALUE", "B-VALUE", "O", "O", "B-COLUMN", "O", "O", "O", "O", "O" ]
417
ship_mission
spider:train_spider.json:4013
What is the most common type of ships?
SELECT TYPE FROM ship GROUP BY TYPE ORDER BY COUNT(*) DESC LIMIT 1
[ "What", "is", "the", "most", "common", "type", "of", "ships", "?" ]
[ { "id": 0, "type": "table", "value": "ship" }, { "id": 1, "type": "column", "value": "type" } ]
[ { "entity_id": 0, "token_idxs": [ 7 ] }, { "entity_id": 1, "token_idxs": [ 5 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "B-COLUMN", "O", "B-TABLE", "O" ]
418
company_office
spider:train_spider.json:4547
What are the names of companies whose headquarters are not "USA"?
SELECT name FROM Companies WHERE Headquarters != 'USA'
[ "What", "are", "the", "names", "of", "companies", "whose", "headquarters", "are", "not", "\"", "USA", "\"", "?" ]
[ { "id": 2, "type": "column", "value": "headquarters" }, { "id": 0, "type": "table", "value": "companies" }, { "id": 1, "type": "column", "value": "name" }, { "id": 3, "type": "value", "value": "USA" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "B-TABLE", "O", "B-COLUMN", "O", "O", "O", "B-VALUE", "O", "O" ]
419
cs_semester
bird:train.json:862
Please list the full names of all the students who took the course Machine Learning Theory.
SELECT T1.f_name, T1.l_name FROM student AS T1 INNER JOIN registration AS T2 ON T1.student_id = T2.student_id INNER JOIN course AS T3 ON T2.course_id = T3.course_id WHERE T3.name = 'Machine Learning Theory'
[ "Please", "list", "the", "full", "names", "of", "all", "the", "students", "who", "took", "the", "course", "Machine", "Learning", "Theory", "." ]
[ { "id": 4, "type": "value", "value": "Machine Learning Theory" }, { "id": 6, "type": "table", "value": "registration" }, { "id": 8, "type": "column", "value": "student_id" }, { "id": 7, "type": "column", "value": "course_id" }, { "id": 5, "type": "table", "value": "student" }, { "id": 0, "type": "column", "value": "f_name" }, { "id": 1, "type": "column", "value": "l_name" }, { "id": 2, "type": "table", "value": "course" }, { "id": 3, "type": "column", "value": "name" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 12 ] }, { "entity_id": 3, "token_idxs": [ 4 ] }, { "entity_id": 4, "token_idxs": [ 13, 14, 15 ] }, { "entity_id": 5, "token_idxs": [ 8 ] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "B-COLUMN", "O", "O", "O", "B-TABLE", "O", "O", "O", "B-TABLE", "B-VALUE", "I-VALUE", "I-VALUE", "O" ]
420
sing_contest
bird:test.json:755
How many songs listed are not performed?
SELECT count(*) FROM songs WHERE id NOT IN ( SELECT songs_id FROM performance_score );
[ "How", "many", "songs", "listed", "are", "not", "performed", "?" ]
[ { "id": 2, "type": "table", "value": "performance_score" }, { "id": 3, "type": "column", "value": "songs_id" }, { "id": 0, "type": "table", "value": "songs" }, { "id": 1, "type": "column", "value": "id" } ]
[ { "entity_id": 0, "token_idxs": [ 2 ] }, { "entity_id": 1, "token_idxs": [] }, { "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", "O", "B-TABLE", "O", "O", "O", "B-TABLE", "O" ]
421
book_review
bird:test.json:598
What are the titles of books that are not "Poet"?
SELECT Title FROM book WHERE TYPE != "Poet"
[ "What", "are", "the", "titles", "of", "books", "that", "are", "not", "\"", "Poet", "\"", "?" ]
[ { "id": 1, "type": "column", "value": "title" }, { "id": 0, "type": "table", "value": "book" }, { "id": 2, "type": "column", "value": "type" }, { "id": 3, "type": "column", "value": "Poet" } ]
[ { "entity_id": 0, "token_idxs": [ 5 ] }, { "entity_id": 1, "token_idxs": [ 3 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 10 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-COLUMN", "O", "B-TABLE", "O", "O", "O", "O", "B-COLUMN", "O", "O" ]
422
superhero
bird:dev.json:727
Who is the publisher of Sauron?
SELECT T2.publisher_name FROM superhero AS T1 INNER JOIN publisher AS T2 ON T1.publisher_id = T2.id WHERE T1.superhero_name = 'Sauron'
[ "Who", "is", "the", "publisher", "of", "Sauron", "?" ]
[ { "id": 0, "type": "column", "value": "publisher_name" }, { "id": 3, "type": "column", "value": "superhero_name" }, { "id": 5, "type": "column", "value": "publisher_id" }, { "id": 1, "type": "table", "value": "superhero" }, { "id": 2, "type": "table", "value": "publisher" }, { "id": 4, "type": "value", "value": "Sauron" }, { "id": 6, "type": "column", "value": "id" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 3 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [ 5 ] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-TABLE", "O", "B-VALUE", "O" ]
423
dorm_1
spider:train_spider.json:5764
Find the first name and age of students who are living in the dorms that do not have amenity TV Lounge.
SELECT T1.fname , T1.age FROM student AS T1 JOIN lives_in AS T2 ON T1.stuid = T2.stuid WHERE T2.dormid NOT IN (SELECT T3.dormid FROM has_amenity AS T3 JOIN dorm_amenity AS T4 ON T3.amenid = T4.amenid WHERE T4.amenity_name = 'TV Lounge')
[ "Find", "the", "first", "name", "and", "age", "of", "students", "who", "are", "living", "in", "the", "dorms", "that", "do", "not", "have", "amenity", "TV", "Lounge", "." ]
[ { "id": 7, "type": "table", "value": "dorm_amenity" }, { "id": 8, "type": "column", "value": "amenity_name" }, { "id": 6, "type": "table", "value": "has_amenity" }, { "id": 9, "type": "value", "value": "TV Lounge" }, { "id": 3, "type": "table", "value": "lives_in" }, { "id": 2, "type": "table", "value": "student" }, { "id": 5, "type": "column", "value": "dormid" }, { "id": 10, "type": "column", "value": "amenid" }, { "id": 0, "type": "column", "value": "fname" }, { "id": 4, "type": "column", "value": "stuid" }, { "id": 1, "type": "column", "value": "age" } ]
[ { "entity_id": 0, "token_idxs": [ 3 ] }, { "entity_id": 1, "token_idxs": [ 5 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 10 ] }, { "entity_id": 4, "token_idxs": [ 7 ] }, { "entity_id": 5, "token_idxs": [ 13 ] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [ 19, 20 ] }, { "entity_id": 10, "token_idxs": [ 18 ] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-COLUMN", "O", "B-COLUMN", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "B-COLUMN", "O", "O", "O", "O", "B-COLUMN", "B-VALUE", "I-VALUE", "O" ]
424
restaurant
bird:train.json:1720
List every city in San Mateo County.
SELECT city FROM geographic WHERE county = 'san mateo county'
[ "List", "every", "city", "in", "San", "Mateo", "County", "." ]
[ { "id": 3, "type": "value", "value": "san mateo county" }, { "id": 0, "type": "table", "value": "geographic" }, { "id": 2, "type": "column", "value": "county" }, { "id": 1, "type": "column", "value": "city" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 2 ] }, { "entity_id": 2, "token_idxs": [ 6 ] }, { "entity_id": 3, "token_idxs": [ 4, 5 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-COLUMN", "O", "B-VALUE", "I-VALUE", "B-COLUMN", "O" ]