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2,147
movie_platform
bird:train.json:111
Which of the film directed by director Abbas Kiarostami has the highest average score?
SELECT T2.movie_title FROM ratings AS T1 INNER JOIN movies AS T2 ON T1.movie_id = T2.movie_id WHERE T2.director_name = 'Abbas Kiarostami' GROUP BY T2.movie_title ORDER BY SUM(T1.rating_score) / COUNT(T1.rating_id) DESC LIMIT 1
[ "Which", "of", "the", "film", "directed", "by", "director", "Abbas", "Kiarostami", "has", "the", "highest", "average", "score", "?" ]
[ { "id": 4, "type": "value", "value": "Abbas Kiarostami" }, { "id": 3, "type": "column", "value": "director_name" }, { "id": 6, "type": "column", "value": "rating_score" }, { "id": 0, "type": "column", "value": "movie_title" }, { "id": 7, "type": "column", "value": "rating_id" }, { "id": 5, "type": "column", "value": "movie_id" }, { "id": 1, "type": "table", "value": "ratings" }, { "id": 2, "type": "table", "value": "movies" } ]
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[ "O", "O", "O", "O", "O", "O", "B-COLUMN", "B-VALUE", "I-VALUE", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O" ]
2,148
car_retails
bird:train.json:1586
Among the German customers, how many of the them has credit limit of zero?
SELECT COUNT(customerNumber) FROM customers WHERE creditLimit = 0 AND country = 'Germany'
[ "Among", "the", "German", "customers", ",", "how", "many", "of", "the", "them", "has", "credit", "limit", "of", "zero", "?" ]
[ { "id": 1, "type": "column", "value": "customernumber" }, { "id": 2, "type": "column", "value": "creditlimit" }, { "id": 0, "type": "table", "value": "customers" }, { "id": 4, "type": "column", "value": "country" }, { "id": 5, "type": "value", "value": "Germany" }, { "id": 3, "type": "value", "value": "0" } ]
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[ "O", "O", "B-VALUE", "B-TABLE", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "O" ]
2,149
synthea
bird:train.json:1395
How many of the patients born in 1920s had pneumonia?
SELECT COUNT(DISTINCT T1.patient) FROM patients AS T1 INNER JOIN conditions AS T2 ON T1.patient = T2.PATIENT WHERE DESCRIPTION = 'Pneumonia' AND strftime('%Y', T1.birthdate) LIKE '192%'
[ "How", "many", "of", "the", "patients", "born", "in", "1920s", "had", "pneumonia", "?" ]
[ { "id": 3, "type": "column", "value": "description" }, { "id": 1, "type": "table", "value": "conditions" }, { "id": 4, "type": "value", "value": "Pneumonia" }, { "id": 7, "type": "column", "value": "birthdate" }, { "id": 0, "type": "table", "value": "patients" }, { "id": 2, "type": "column", "value": "patient" }, { "id": 5, "type": "value", "value": "192%" }, { "id": 6, "type": "value", "value": "%Y" } ]
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[ "O", "O", "O", "O", "B-COLUMN", "O", "O", "B-VALUE", "O", "B-VALUE", "O" ]
2,150
debit_card_specializing
bird:dev.json:1500
Please list the product description of the products consumed in September, 2013.
SELECT T3.Description FROM transactions_1k AS T1 INNER JOIN yearmonth AS T2 ON T1.CustomerID = T2.CustomerID INNER JOIN products AS T3 ON T1.ProductID = T3.ProductID WHERE T2.Date = '201309'
[ "Please", "list", "the", "product", "description", "of", "the", "products", "consumed", "in", "September", ",", "2013", "." ]
[ { "id": 4, "type": "table", "value": "transactions_1k" }, { "id": 0, "type": "column", "value": "description" }, { "id": 7, "type": "column", "value": "customerid" }, { "id": 5, "type": "table", "value": "yearmonth" }, { "id": 6, "type": "column", "value": "productid" }, { "id": 1, "type": "table", "value": "products" }, { "id": 3, "type": "value", "value": "201309" }, { "id": 2, "type": "column", "value": "date" } ]
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[ "O", "O", "O", "B-COLUMN", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "O", "O", "B-VALUE", "O" ]
2,151
flight_company
spider:train_spider.json:6371
What are the names and types of the companies that have ever operated a flight?
SELECT T1.name , T1.type FROM operate_company AS T1 JOIN flight AS t2 ON T1.id = T2.company_id
[ "What", "are", "the", "names", "and", "types", "of", "the", "companies", "that", "have", "ever", "operated", "a", "flight", "?" ]
[ { "id": 2, "type": "table", "value": "operate_company" }, { "id": 5, "type": "column", "value": "company_id" }, { "id": 3, "type": "table", "value": "flight" }, { "id": 0, "type": "column", "value": "name" }, { "id": 1, "type": "column", "value": "type" }, { "id": 4, "type": "column", "value": "id" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "B-COLUMN", "O", "O", "B-COLUMN", "O", "O", "O", "B-TABLE", "I-TABLE", "B-TABLE", "O" ]
2,152
public_review_platform
bird:train.json:3889
Count the active businesses that has an attribute of caters with low review count.
SELECT COUNT(T1.business_id) FROM Business AS T1 INNER JOIN Business_Attributes AS T2 ON T1.business_id = T2.business_id INNER JOIN Attributes AS T3 ON T2.attribute_id = T3.attribute_id WHERE T3.attribute_name LIKE 'Caters' AND T1.review_count LIKE 'Low' AND T1.active LIKE 'TRUE'
[ "Count", "the", "active", "businesses", "that", "has", "an", "attribute", "of", "caters", "with", "low", "review", "count", "." ]
[ { "id": 3, "type": "table", "value": "business_attributes" }, { "id": 5, "type": "column", "value": "attribute_name" }, { "id": 4, "type": "column", "value": "attribute_id" }, { "id": 7, "type": "column", "value": "review_count" }, { "id": 1, "type": "column", "value": "business_id" }, { "id": 0, "type": "table", "value": "attributes" }, { "id": 2, "type": "table", "value": "business" }, { "id": 6, "type": "value", "value": "Caters" }, { "id": 9, "type": "column", "value": "active" }, { "id": 10, "type": "value", "value": "TRUE" }, { "id": 8, "type": "value", "value": "Low" } ]
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[ "O", "O", "B-COLUMN", "B-TABLE", "O", "O", "O", "B-VALUE", "O", "B-VALUE", "O", "B-VALUE", "B-COLUMN", "I-COLUMN", "O" ]
2,153
art_1
bird:test.json:1214
Find the names and years of all sculptures that are not located in gallery 226.
SELECT title , YEAR FROM sculptures WHERE LOCATION != "Gallery 226"
[ "Find", "the", "names", "and", "years", "of", "all", "sculptures", "that", "are", "not", "located", "in", "gallery", "226", "." ]
[ { "id": 4, "type": "column", "value": "Gallery 226" }, { "id": 0, "type": "table", "value": "sculptures" }, { "id": 3, "type": "column", "value": "location" }, { "id": 1, "type": "column", "value": "title" }, { "id": 2, "type": "column", "value": "year" } ]
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[ "O", "O", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "O", "B-COLUMN", "I-COLUMN", "B-COLUMN", "I-COLUMN", "O" ]
2,154
railway
spider:train_spider.json:5643
Show the id and builder of the railway that are associated with the most trains.
SELECT T2.Railway_ID , T1.Builder FROM railway AS T1 JOIN train AS T2 ON T1.Railway_ID = T2.Railway_ID GROUP BY T2.Railway_ID ORDER BY COUNT(*) DESC LIMIT 1
[ "Show", "the", "i", "d", "and", "builder", "of", "the", "railway", "that", "are", "associated", "with", "the", "most", "trains", "." ]
[ { "id": 0, "type": "column", "value": "railway_id" }, { "id": 1, "type": "column", "value": "builder" }, { "id": 2, "type": "table", "value": "railway" }, { "id": 3, "type": "table", "value": "train" } ]
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[ "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "B-TABLE", "O" ]
2,155
sakila_1
spider:train_spider.json:2966
Which film has the most copies in the inventory? List both title and id.
SELECT T1.title , T1.film_id FROM film AS T1 JOIN inventory AS T2 ON T1.film_id = T2.film_id GROUP BY T1.film_id ORDER BY count(*) DESC LIMIT 1
[ "Which", "film", "has", "the", "most", "copies", "in", "the", "inventory", "?", "List", "both", "title", "and", "i", "d." ]
[ { "id": 3, "type": "table", "value": "inventory" }, { "id": 0, "type": "column", "value": "film_id" }, { "id": 1, "type": "column", "value": "title" }, { "id": 2, "type": "table", "value": "film" } ]
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[ "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "B-COLUMN", "O", "O", "O" ]
2,156
shipping
bird:train.json:5620
Identify the total weight of shipments transported in 2016 by the newest Peterbilt.
SELECT SUM(T2.weight) FROM truck AS T1 INNER JOIN shipment AS T2 ON T1.truck_id = T2.truck_id WHERE T1.make = 'Peterbilt' AND STRFTIME('%Y', T2.ship_date) = '2016' ORDER BY T1.model_year DESC LIMIT 1
[ "Identify", "the", "total", "weight", "of", "shipments", "transported", "in", "2016", "by", "the", "newest", "Peterbilt", "." ]
[ { "id": 2, "type": "column", "value": "model_year" }, { "id": 6, "type": "value", "value": "Peterbilt" }, { "id": 9, "type": "column", "value": "ship_date" }, { "id": 1, "type": "table", "value": "shipment" }, { "id": 4, "type": "column", "value": "truck_id" }, { "id": 3, "type": "column", "value": "weight" }, { "id": 0, "type": "table", "value": "truck" }, { "id": 5, "type": "column", "value": "make" }, { "id": 7, "type": "value", "value": "2016" }, { "id": 8, "type": "value", "value": "%Y" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "B-TABLE", "O", "O", "B-VALUE", "O", "O", "O", "B-VALUE", "O" ]
2,157
formula_1
spider:train_spider.json:2186
What are the first names of the different drivers who won in position 1 as driver standing and had more than 20 points?
SELECT DISTINCT T1.forename FROM drivers AS T1 JOIN driverstandings AS T2 ON T1.driverid = T2.driverid WHERE T2.position = 1 AND T2.wins = 1 AND T2.points > 20
[ "What", "are", "the", "first", "names", "of", "the", "different", "drivers", "who", "won", "in", "position", "1", "as", "driver", "standing", "and", "had", "more", "than", "20", "points", "?" ]
[ { "id": 2, "type": "table", "value": "driverstandings" }, { "id": 0, "type": "column", "value": "forename" }, { "id": 3, "type": "column", "value": "driverid" }, { "id": 4, "type": "column", "value": "position" }, { "id": 1, "type": "table", "value": "drivers" }, { "id": 7, "type": "column", "value": "points" }, { "id": 6, "type": "column", "value": "wins" }, { "id": 8, "type": "value", "value": "20" }, { "id": 5, "type": "value", "value": "1" } ]
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2,158
legislator
bird:train.json:4851
What is the ratio between male and female legislators?
SELECT CAST(SUM(CASE WHEN gender_bio = 'M' THEN 1 ELSE 0 END) AS REAL) / SUM(CASE WHEN gender_bio = 'F' THEN 1 ELSE 0 END) FROM historical
[ "What", "is", "the", "ratio", "between", "male", "and", "female", "legislators", "?" ]
[ { "id": 0, "type": "table", "value": "historical" }, { "id": 3, "type": "column", "value": "gender_bio" }, { "id": 1, "type": "value", "value": "0" }, { "id": 2, "type": "value", "value": "1" }, { "id": 4, "type": "value", "value": "F" }, { "id": 5, "type": "value", "value": "M" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "O", "O", "O", "O", "O" ]
2,159
thrombosis_prediction
bird:dev.json:1183
For the patient who was diagnosed SLE on 1997/1/27, what was his/her original diagnose when he/she came to the hospital for the first time?
SELECT T1.Diagnosis FROM Patient AS T1 INNER JOIN Examination AS T2 ON T1.ID = T2.ID WHERE T1.ID = ( SELECT ID FROM Examination WHERE `Examination Date` = '1997-01-27' AND Diagnosis = 'SLE' ) AND T2.`Examination Date` = T1.`First Date`
[ "For", "the", "patient", "who", "was", "diagnosed", "SLE", "on", "1997/1/27", ",", "what", "was", "his", "/", "her", "original", "diagnose", "when", "he", "/", "she", "came", "to", "the", "hospital", "for", "the", "first", "time", "?" ]
[ { "id": 4, "type": "column", "value": "Examination Date" }, { "id": 2, "type": "table", "value": "examination" }, { "id": 5, "type": "column", "value": "First Date" }, { "id": 6, "type": "value", "value": "1997-01-27" }, { "id": 0, "type": "column", "value": "diagnosis" }, { "id": 1, "type": "table", "value": "patient" }, { "id": 7, "type": "value", "value": "SLE" }, { "id": 3, "type": "column", "value": "id" } ]
[ { "entity_id": 0, "token_idxs": [ 16 ] }, { "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": [ 27, 28 ] }, { "entity_id": 6, "token_idxs": [ 8 ] }, { "entity_id": 7, "token_idxs": [ 6 ] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-TABLE", "O", "O", "O", "B-VALUE", "O", "B-VALUE", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O" ]
2,160
formula_1
spider:train_spider.json:2226
What is the average fastest lap speed for races held after 2004, for each race, ordered by year?
SELECT avg(T2.fastestlapspeed) , T1.name , T1.year FROM races AS T1 JOIN results AS T2 ON T1.raceid = T2.raceid WHERE T1.year > 2014 GROUP BY T1.name ORDER BY T1.year
[ "What", "is", "the", "average", "fastest", "lap", "speed", "for", "races", "held", "after", "2004", ",", "for", "each", "race", ",", "ordered", "by", "year", "?" ]
[ { "id": 5, "type": "column", "value": "fastestlapspeed" }, { "id": 3, "type": "table", "value": "results" }, { "id": 6, "type": "column", "value": "raceid" }, { "id": 2, "type": "table", "value": "races" }, { "id": 0, "type": "column", "value": "name" }, { "id": 1, "type": "column", "value": "year" }, { "id": 4, "type": "value", "value": "2014" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 19 ] }, { "entity_id": 2, "token_idxs": [ 8 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [ 11 ] }, { "entity_id": 5, "token_idxs": [ 4, 5, 6 ] }, { "entity_id": 6, "token_idxs": [ 15 ] }, { "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", "I-COLUMN", "I-COLUMN", "O", "B-TABLE", "O", "O", "B-VALUE", "O", "O", "O", "B-COLUMN", "O", "O", "O", "B-COLUMN", "O" ]
2,161
department_store
spider:train_spider.json:4768
What are the ids of all products that were either ordered more than 3 times or have a cumulative amount purchased of above 80000?
SELECT product_id FROM Order_Items GROUP BY product_id HAVING count(*) > 3 UNION SELECT product_id FROM Product_Suppliers GROUP BY product_id HAVING sum(total_amount_purchased) > 80000
[ "What", "are", "the", "ids", "of", "all", "products", "that", "were", "either", "ordered", "more", "than", "3", "times", "or", "have", "a", "cumulative", "amount", "purchased", "of", "above", "80000", "?" ]
[ { "id": 5, "type": "column", "value": "total_amount_purchased" }, { "id": 2, "type": "table", "value": "product_suppliers" }, { "id": 0, "type": "table", "value": "order_items" }, { "id": 1, "type": "column", "value": "product_id" }, { "id": 4, "type": "value", "value": "80000" }, { "id": 3, "type": "value", "value": "3" } ]
[ { "entity_id": 0, "token_idxs": [ 10 ] }, { "entity_id": 1, "token_idxs": [ 6 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 13 ] }, { "entity_id": 4, "token_idxs": [ 23 ] }, { "entity_id": 5, "token_idxs": [ 19, 20 ] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "O", "B-TABLE", "O", "O", "B-VALUE", "O", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "B-VALUE", "O" ]
2,162
insurance_policies
spider:train_spider.json:3868
How many settlements were made on the claim with the most recent claim settlement date? List the number and the claim id.
SELECT count(*) , T1.claim_id FROM Claims AS T1 JOIN Settlements AS T2 ON T1.claim_id = T2.claim_id GROUP BY T1.claim_id ORDER BY T1.Date_Claim_Settled DESC LIMIT 1
[ "How", "many", "settlements", "were", "made", "on", "the", "claim", "with", "the", "most", "recent", "claim", "settlement", "date", "?", "List", "the", "number", "and", "the", "claim", "i", "d." ]
[ { "id": 3, "type": "column", "value": "date_claim_settled" }, { "id": 2, "type": "table", "value": "settlements" }, { "id": 0, "type": "column", "value": "claim_id" }, { "id": 1, "type": "table", "value": "claims" } ]
[ { "entity_id": 0, "token_idxs": [ 21, 22, 23 ] }, { "entity_id": 1, "token_idxs": [ 12 ] }, { "entity_id": 2, "token_idxs": [ 2 ] }, { "entity_id": 3, "token_idxs": [ 13 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "B-COLUMN", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN" ]
2,163
student_club
bird:dev.json:1410
List out the full name and total cost that member id "rec4BLdZHS2Blfp4v" incurred?
SELECT T1.first_name, T1.last_name, SUM(T2.cost) FROM member AS T1 INNER JOIN expense AS T2 ON T1.member_id = T2.link_to_member WHERE T1.member_id = 'rec4BLdZHS2Blfp4v'
[ "List", "out", "the", "full", "name", "and", "total", "cost", "that", "member", "i", "d", "\"", "rec4BLdZHS2Blfp4v", "\"", "incurred", "?" ]
[ { "id": 5, "type": "value", "value": "rec4BLdZHS2Blfp4v" }, { "id": 7, "type": "column", "value": "link_to_member" }, { "id": 0, "type": "column", "value": "first_name" }, { "id": 1, "type": "column", "value": "last_name" }, { "id": 4, "type": "column", "value": "member_id" }, { "id": 3, "type": "table", "value": "expense" }, { "id": 2, "type": "table", "value": "member" }, { "id": 6, "type": "column", "value": "cost" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 4 ] }, { "entity_id": 2, "token_idxs": [ 9 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [ 10, 11 ] }, { "entity_id": 5, "token_idxs": [ 13 ] }, { "entity_id": 6, "token_idxs": [ 7 ] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "B-COLUMN", "O", "O", "B-COLUMN", "O", "B-TABLE", "B-COLUMN", "I-COLUMN", "O", "B-VALUE", "O", "O", "O" ]
2,164
legislator
bird:train.json:4849
How many districts are in Idaho?
SELECT COUNT(district) FROM `current-terms` WHERE state = 'ID'
[ "How", "many", "districts", "are", "in", "Idaho", "?" ]
[ { "id": 0, "type": "table", "value": "current-terms" }, { "id": 3, "type": "column", "value": "district" }, { "id": 1, "type": "column", "value": "state" }, { "id": 2, "type": "value", "value": "ID" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 2 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-COLUMN", "O", "O", "O", "O" ]
2,165
donor
bird:train.json:3299
List all the items from "Sax Arts & Crafts" and the zip code of the schools that received them.
SELECT T2.school_zip, T1.item_name FROM resources AS T1 INNER JOIN projects AS T2 ON T1.projectid = T2.projectid WHERE T1.vendor_name = 'Sax Arts & Crafts'
[ "List", "all", "the", "items", "from", "\"", "Sax", "Arts", "&", "Crafts", "\"", "and", "the", "zip", "code", "of", "the", "schools", "that", "received", "them", "." ]
[ { "id": 5, "type": "value", "value": "Sax Arts & Crafts" }, { "id": 4, "type": "column", "value": "vendor_name" }, { "id": 0, "type": "column", "value": "school_zip" }, { "id": 1, "type": "column", "value": "item_name" }, { "id": 2, "type": "table", "value": "resources" }, { "id": 6, "type": "column", "value": "projectid" }, { "id": 3, "type": "table", "value": "projects" } ]
[ { "entity_id": 0, "token_idxs": [ 17 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [ 6, 7, 8, 9 ] }, { "entity_id": 6, "token_idxs": [ 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", "O", "O", "O", "O", "B-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "B-COLUMN", "O", "O" ]
2,166
wine_1
spider:train_spider.json:6528
What are the areas and counties for all appelations?
SELECT Area , County FROM APPELLATIONS
[ "What", "are", "the", "areas", "and", "counties", "for", "all", "appelations", "?" ]
[ { "id": 0, "type": "table", "value": "appellations" }, { "id": 2, "type": "column", "value": "county" }, { "id": 1, "type": "column", "value": "area" } ]
[ { "entity_id": 0, "token_idxs": [ 8 ] }, { "entity_id": 1, "token_idxs": [ 3 ] }, { "entity_id": 2, "token_idxs": [ 5 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-COLUMN", "O", "B-COLUMN", "O", "O", "B-TABLE", "O" ]
2,168
image_and_language
bird:train.json:7497
What is the relationship between object sample no.12 and no.8 of image no.2345511?
SELECT T1.PRED_CLASS FROM PRED_CLASSES AS T1 INNER JOIN IMG_REL AS T2 ON T1.PRED_CLASS_ID = T2.PRED_CLASS_ID WHERE T2.IMG_ID = 2345511 AND T2.OBJ1_SAMPLE_ID = 12 AND T2.OBJ2_SAMPLE_ID = 8
[ "What", "is", "the", "relationship", "between", "object", "sample", "no.12", "and", "no.8", "of", "image", "no.2345511", "?" ]
[ { "id": 6, "type": "column", "value": "obj1_sample_id" }, { "id": 8, "type": "column", "value": "obj2_sample_id" }, { "id": 3, "type": "column", "value": "pred_class_id" }, { "id": 1, "type": "table", "value": "pred_classes" }, { "id": 0, "type": "column", "value": "pred_class" }, { "id": 2, "type": "table", "value": "img_rel" }, { "id": 5, "type": "value", "value": "2345511" }, { "id": 4, "type": "column", "value": "img_id" }, { "id": 7, "type": "value", "value": "12" }, { "id": 9, "type": "value", "value": "8" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 11 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [ 12 ] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [ 5, 6 ] }, { "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", "I-COLUMN", "O", "O", "O", "O", "B-TABLE", "B-VALUE", "O" ]
2,169
student_assessment
spider:train_spider.json:98
What are the first names of the people in alphabetical order?
SELECT first_name FROM people ORDER BY first_name
[ "What", "are", "the", "first", "names", "of", "the", "people", "in", "alphabetical", "order", "?" ]
[ { "id": 1, "type": "column", "value": "first_name" }, { "id": 0, "type": "table", "value": "people" } ]
[ { "entity_id": 0, "token_idxs": [ 7 ] }, { "entity_id": 1, "token_idxs": [ 3, 4 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "B-TABLE", "O", "O", "O", "O" ]
2,171
loan_1
spider:train_spider.json:3016
Find the city and state of the bank branch named morningside.
SELECT city , state FROM bank WHERE bname = 'morningside'
[ "Find", "the", "city", "and", "state", "of", "the", "bank", "branch", "named", "morningside", "." ]
[ { "id": 4, "type": "value", "value": "morningside" }, { "id": 2, "type": "column", "value": "state" }, { "id": 3, "type": "column", "value": "bname" }, { "id": 0, "type": "table", "value": "bank" }, { "id": 1, "type": "column", "value": "city" } ]
[ { "entity_id": 0, "token_idxs": [ 7 ] }, { "entity_id": 1, "token_idxs": [ 2 ] }, { "entity_id": 2, "token_idxs": [ 4 ] }, { "entity_id": 3, "token_idxs": [ 9 ] }, { "entity_id": 4, "token_idxs": [ 10 ] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-COLUMN", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "B-COLUMN", "B-VALUE", "O" ]
2,172
shakespeare
bird:train.json:3032
What are the work numbers that are related to King Henry?
SELECT id FROM works WHERE Title LIKE '%Henry%'
[ "What", "are", "the", "work", "numbers", "that", "are", "related", "to", "King", "Henry", "?" ]
[ { "id": 3, "type": "value", "value": "%Henry%" }, { "id": 0, "type": "table", "value": "works" }, { "id": 2, "type": "column", "value": "title" }, { "id": 1, "type": "column", "value": "id" } ]
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[ "O", "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "B-VALUE", "O" ]
2,173
public_review_platform
bird:train.json:3903
How many businesses in Phoenix, Arizona is attributed to waiter service?
SELECT COUNT(T1.business_id) FROM Business AS T1 INNER JOIN Business_attributes AS T2 ON T1.business_id = T2.business_id INNER JOIN Attributes AS T3 ON T2.attribute_id = T3.attribute_id WHERE T1.city LIKE 'Phoenix' AND T3.attribute_name LIKE 'waiter_service' AND T2.attribute_id = 2
[ "How", "many", "businesses", "in", "Phoenix", ",", "Arizona", "is", "attributed", "to", "waiter", "service", "?" ]
[ { "id": 3, "type": "table", "value": "business_attributes" }, { "id": 7, "type": "column", "value": "attribute_name" }, { "id": 8, "type": "value", "value": "waiter_service" }, { "id": 4, "type": "column", "value": "attribute_id" }, { "id": 1, "type": "column", "value": "business_id" }, { "id": 0, "type": "table", "value": "attributes" }, { "id": 2, "type": "table", "value": "business" }, { "id": 6, "type": "value", "value": "Phoenix" }, { "id": 5, "type": "column", "value": "city" }, { "id": 9, "type": "value", "value": "2" } ]
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[ "O", "O", "B-TABLE", "O", "B-VALUE", "O", "O", "O", "B-TABLE", "O", "B-VALUE", "I-VALUE", "O" ]
2,174
food_inspection_2
bird:train.json:6179
Provide the names and inspection results of the facilities located in Burnham.
SELECT DISTINCT T1.dba_name, T2.results FROM establishment AS T1 INNER JOIN inspection AS T2 ON T1.license_no = T2.license_no WHERE T1.city = 'BURNHAM'
[ "Provide", "the", "names", "and", "inspection", "results", "of", "the", "facilities", "located", "in", "Burnham", "." ]
[ { "id": 2, "type": "table", "value": "establishment" }, { "id": 3, "type": "table", "value": "inspection" }, { "id": 6, "type": "column", "value": "license_no" }, { "id": 0, "type": "column", "value": "dba_name" }, { "id": 1, "type": "column", "value": "results" }, { "id": 5, "type": "value", "value": "BURNHAM" }, { "id": 4, "type": "column", "value": "city" } ]
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[ "O", "O", "B-COLUMN", "O", "B-TABLE", "B-COLUMN", "O", "O", "O", "O", "O", "B-VALUE", "O" ]
2,175
world_development_indicators
bird:train.json:2094
Among the countries with note on the series code SM.POP.TOTL, how many of them are in the low-income group?
SELECT COUNT(T1.Countrycode) FROM Country AS T1 INNER JOIN CountryNotes AS T2 ON T1.CountryCode = T2.Countrycode WHERE T2.Seriescode = 'SM.POP.TOTL' AND T1.IncomeGroup = 'Low income'
[ "Among", "the", "countries", "with", "note", "on", "the", "series", "code", "SM.POP.TOTL", ",", "how", "many", "of", "them", "are", "in", "the", "low", "-", "income", "group", "?" ]
[ { "id": 1, "type": "table", "value": "countrynotes" }, { "id": 2, "type": "column", "value": "countrycode" }, { "id": 4, "type": "value", "value": "SM.POP.TOTL" }, { "id": 5, "type": "column", "value": "incomegroup" }, { "id": 3, "type": "column", "value": "seriescode" }, { "id": 6, "type": "value", "value": "Low income" }, { "id": 0, "type": "table", "value": "country" } ]
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[ "O", "O", "B-TABLE", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "B-VALUE", "O", "O", "O", "O", "O", "O", "O", "O", "B-VALUE", "I-VALUE", "I-VALUE", "B-COLUMN", "O" ]
2,176
farm
spider:train_spider.json:27
Return the maximum and minimum number of cows across all farms.
SELECT max(Cows) , min(Cows) FROM farm
[ "Return", "the", "maximum", "and", "minimum", "number", "of", "cows", "across", "all", "farms", "." ]
[ { "id": 0, "type": "table", "value": "farm" }, { "id": 1, "type": "column", "value": "cows" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O" ]
2,177
toxicology
bird:dev.json:314
How many single bonds are there in the list?
SELECT COUNT(T.bond_id) FROM bond AS T WHERE T.bond_type = '-'
[ "How", "many", "single", "bonds", "are", "there", "in", "the", "list", "?" ]
[ { "id": 1, "type": "column", "value": "bond_type" }, { "id": 3, "type": "column", "value": "bond_id" }, { "id": 0, "type": "table", "value": "bond" }, { "id": 2, "type": "value", "value": "-" } ]
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[ "O", "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "O" ]
2,178
student_loan
bird:train.json:4469
How many female students have no payment due?
SELECT COUNT(name) FROM no_payment_due WHERE name NOT IN ( SELECT name FROM male )
[ "How", "many", "female", "students", "have", "no", "payment", "due", "?" ]
[ { "id": 0, "type": "table", "value": "no_payment_due" }, { "id": 1, "type": "column", "value": "name" }, { "id": 2, "type": "table", "value": "male" } ]
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[ "O", "O", "B-TABLE", "O", "O", "B-TABLE", "I-TABLE", "I-TABLE", "O" ]
2,179
world_development_indicators
bird:train.json:2182
Which country have completed vital registration? List all the countries.
SELECT ShortName FROM Country WHERE VitalRegistrationComplete = 'Yes'
[ "Which", "country", "have", "completed", "vital", "registration", "?", "List", "all", "the", "countries", "." ]
[ { "id": 2, "type": "column", "value": "vitalregistrationcomplete" }, { "id": 1, "type": "column", "value": "shortname" }, { "id": 0, "type": "table", "value": "country" }, { "id": 3, "type": "value", "value": "Yes" } ]
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[ "O", "B-TABLE", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "O", "O", "O", "O" ]
2,180
music_2
spider:train_spider.json:5174
What are all the labels?
SELECT DISTINCT label FROM Albums
[ "What", "are", "all", "the", "labels", "?" ]
[ { "id": 0, "type": "table", "value": "albums" }, { "id": 1, "type": "column", "value": "label" } ]
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[ "O", "O", "O", "O", "B-COLUMN", "O" ]
2,181
student_loan
bird:train.json:4459
Find the percentage of male students enlisted in the fire department.
SELECT CAST(COUNT(T2.name) AS REAL) * 100 / COUNT(T1.name) FROM enlist AS T1 LEFT JOIN male AS T2 ON T1.name = T2.name WHERE T1.organ = 'fire_department'
[ "Find", "the", "percentage", "of", "male", "students", "enlisted", "in", "the", "fire", "department", "." ]
[ { "id": 3, "type": "value", "value": "fire_department" }, { "id": 0, "type": "table", "value": "enlist" }, { "id": 2, "type": "column", "value": "organ" }, { "id": 1, "type": "table", "value": "male" }, { "id": 4, "type": "column", "value": "name" }, { "id": 5, "type": "value", "value": "100" } ]
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[ "O", "O", "O", "O", "B-TABLE", "O", "B-TABLE", "O", "O", "B-VALUE", "I-VALUE", "O" ]
2,182
vehicle_rent
bird:test.json:402
What are the names, types of powertrains, and yearly fuel costs for vehicles with model years in either 2013 2014?
SELECT name , type_of_powertrain , annual_fuel_cost FROM vehicles WHERE model_year = 2013 OR model_year = 2014
[ "What", "are", "the", "names", ",", "types", "of", "powertrains", ",", "and", "yearly", "fuel", "costs", "for", "vehicles", "with", "model", "years", "in", "either", "2013", "2014", "?" ]
[ { "id": 2, "type": "column", "value": "type_of_powertrain" }, { "id": 3, "type": "column", "value": "annual_fuel_cost" }, { "id": 4, "type": "column", "value": "model_year" }, { "id": 0, "type": "table", "value": "vehicles" }, { "id": 1, "type": "column", "value": "name" }, { "id": 5, "type": "value", "value": "2013" }, { "id": 6, "type": "value", "value": "2014" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "I-COLUMN", "O", "B-TABLE", "O", "B-COLUMN", "I-COLUMN", "O", "O", "B-VALUE", "B-VALUE", "O" ]
2,183
world
bird:train.json:7912
List down the languages of countries with an independence year between 1980 to 1995.
SELECT T2.Name, T1.Language FROM CountryLanguage AS T1 INNER JOIN Country AS T2 ON T1.CountryCode = T2.Code WHERE T2.IndepYear BETWEEN 1980 AND 1995
[ "List", "down", "the", "languages", "of", "countries", "with", "an", "independence", "year", "between", "1980", "to", "1995", "." ]
[ { "id": 2, "type": "table", "value": "countrylanguage" }, { "id": 7, "type": "column", "value": "countrycode" }, { "id": 4, "type": "column", "value": "indepyear" }, { "id": 1, "type": "column", "value": "language" }, { "id": 3, "type": "table", "value": "country" }, { "id": 0, "type": "column", "value": "name" }, { "id": 5, "type": "value", "value": "1980" }, { "id": 6, "type": "value", "value": "1995" }, { "id": 8, "type": "column", "value": "code" } ]
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[ "O", "B-TABLE", "I-TABLE", "B-COLUMN", "O", "B-TABLE", "O", "O", "B-COLUMN", "I-COLUMN", "O", "B-VALUE", "O", "B-VALUE", "O" ]
2,185
customers_card_transactions
spider:train_spider.json:667
How many accounts do we have?
SELECT count(*) FROM Accounts
[ "How", "many", "accounts", "do", "we", "have", "?" ]
[ { "id": 0, "type": "table", "value": "accounts" } ]
[ { "entity_id": 0, "token_idxs": [ 2 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-TABLE", "O", "O", "O", "O" ]
2,186
soccer_2016
bird:train.json:1829
How many Orange Cap awards were won by CH Gayle?
SELECT SUM(CASE WHEN T1.Player_Name = 'CH Gayle' THEN 1 ELSE 0 END) AS cnt FROM Player AS T1 INNER JOIN Season AS T2 ON T1.Player_Id = T2.Orange_Cap
[ "How", "many", "Orange", "Cap", "awards", "were", "won", "by", "CH", "Gayle", "?" ]
[ { "id": 6, "type": "column", "value": "player_name" }, { "id": 3, "type": "column", "value": "orange_cap" }, { "id": 2, "type": "column", "value": "player_id" }, { "id": 7, "type": "value", "value": "CH Gayle" }, { "id": 0, "type": "table", "value": "player" }, { "id": 1, "type": "table", "value": "season" }, { "id": 4, "type": "value", "value": "0" }, { "id": 5, "type": "value", "value": "1" } ]
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[ "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "O", "O", "B-VALUE", "I-VALUE", "O" ]
2,187
conference
bird:test.json:1065
Show all locations where at least two conferences are located.
SELECT LOCATION FROM conference GROUP BY LOCATION HAVING count(*) >= 2
[ "Show", "all", "locations", "where", "at", "least", "two", "conferences", "are", "located", "." ]
[ { "id": 0, "type": "table", "value": "conference" }, { "id": 1, "type": "column", "value": "location" }, { "id": 2, "type": "value", "value": "2" } ]
[ { "entity_id": 0, "token_idxs": [ 7 ] }, { "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", "B-TABLE", "O", "O", "O" ]
2,188
department_store
spider:train_spider.json:4758
Return the names and ids of customers who have TN in their address.
SELECT customer_name , customer_id FROM customers WHERE customer_address LIKE "%TN%"
[ "Return", "the", "names", "and", "ids", "of", "customers", "who", "have", "TN", "in", "their", "address", "." ]
[ { "id": 3, "type": "column", "value": "customer_address" }, { "id": 1, "type": "column", "value": "customer_name" }, { "id": 2, "type": "column", "value": "customer_id" }, { "id": 0, "type": "table", "value": "customers" }, { "id": 4, "type": "column", "value": "%TN%" } ]
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[ "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "B-COLUMN", "O", "O", "O", "O" ]
2,189
image_and_language
bird:train.json:7473
How many object samples are there in image no.1?
SELECT COUNT(OBJ_SAMPLE_ID) FROM IMG_OBJ WHERE IMG_ID = 1
[ "How", "many", "object", "samples", "are", "there", "in", "image", "no.1", "?" ]
[ { "id": 3, "type": "column", "value": "obj_sample_id" }, { "id": 0, "type": "table", "value": "img_obj" }, { "id": 1, "type": "column", "value": "img_id" }, { "id": 2, "type": "value", "value": "1" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 2, 3 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "O", "O", "O", "O" ]
2,190
legislator
bird:train.json:4785
Give the Wikipedia IDs of historical legislators who are Readjuster Democrats.
SELECT T2.wikipedia_id FROM `historical-terms` AS T1 INNER JOIN historical AS T2 ON T2.bioguide_id = T1.bioguide WHERE T1.party = 'Readjuster Democrat'
[ "Give", "the", "Wikipedia", "IDs", "of", "historical", "legislators", "who", "are", "Readjuster", "Democrats", "." ]
[ { "id": 4, "type": "value", "value": "Readjuster Democrat" }, { "id": 1, "type": "table", "value": "historical-terms" }, { "id": 0, "type": "column", "value": "wikipedia_id" }, { "id": 5, "type": "column", "value": "bioguide_id" }, { "id": 2, "type": "table", "value": "historical" }, { "id": 6, "type": "column", "value": "bioguide" }, { "id": 3, "type": "column", "value": "party" } ]
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[ "O", "O", "B-COLUMN", "I-COLUMN", "O", "B-TABLE", "O", "O", "O", "B-VALUE", "I-VALUE", "O" ]
2,191
movie_3
bird:train.json:9391
List at least 5 customers who paid greater than $10. Provide the full name of the customers.
SELECT T2.first_name, T2.last_name FROM payment AS T1 INNER JOIN customer AS T2 ON T1.customer_id = T2.customer_id WHERE T1.amount > 10
[ "List", "at", "least", "5", "customers", "who", "paid", "greater", "than", "$", "10", ".", "Provide", "the", "full", "name", "of", "the", "customers", "." ]
[ { "id": 6, "type": "column", "value": "customer_id" }, { "id": 0, "type": "column", "value": "first_name" }, { "id": 1, "type": "column", "value": "last_name" }, { "id": 3, "type": "table", "value": "customer" }, { "id": 2, "type": "table", "value": "payment" }, { "id": 4, "type": "column", "value": "amount" }, { "id": 5, "type": "value", "value": "10" } ]
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[ "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "B-VALUE", "O", "O", "O", "O", "B-COLUMN", "O", "O", "B-COLUMN", "O" ]
2,192
books
bird:train.json:6066
Write the full name of the customers whose address is at 55 Dorton Pass, Huangqiao.
SELECT DISTINCT T1.first_name, T1.last_name FROM customer AS T1 INNER JOIN customer_address AS T2 ON T1.customer_id = T2.customer_id INNER JOIN address AS T3 ON T3.address_id = T2.address_id WHERE T3.street_number = 55 AND T3.street_name = 'Dorton Pass' AND T3.city = 'Huangqiao'
[ "Write", "the", "full", "name", "of", "the", "customers", "whose", "address", "is", "at", "55", "Dorton", "Pass", ",", "Huangqiao", "." ]
[ { "id": 4, "type": "table", "value": "customer_address" }, { "id": 6, "type": "column", "value": "street_number" }, { "id": 8, "type": "column", "value": "street_name" }, { "id": 9, "type": "value", "value": "Dorton Pass" }, { "id": 12, "type": "column", "value": "customer_id" }, { "id": 0, "type": "column", "value": "first_name" }, { "id": 5, "type": "column", "value": "address_id" }, { "id": 1, "type": "column", "value": "last_name" }, { "id": 11, "type": "value", "value": "Huangqiao" }, { "id": 3, "type": "table", "value": "customer" }, { "id": 2, "type": "table", "value": "address" }, { "id": 10, "type": "column", "value": "city" }, { "id": 7, "type": "value", "value": "55" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "B-TABLE", "B-TABLE", "O", "O", "B-VALUE", "B-VALUE", "I-VALUE", "O", "B-VALUE", "O" ]
2,193
game_1
spider:train_spider.json:6001
How many students play sports?
SELECT count(DISTINCT StuID) FROM Sportsinfo
[ "How", "many", "students", "play", "sports", "?" ]
[ { "id": 0, "type": "table", "value": "sportsinfo" }, { "id": 1, "type": "column", "value": "stuid" } ]
[ { "entity_id": 0, "token_idxs": [ 4 ] }, { "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", "B-TABLE", "O" ]
2,194
world_development_indicators
bird:train.json:2245
What is the percentage of increase of the indicator on Adolescent fertility rate from 1960 to 1961 in the country whose Alpha2Code is 1A?
SELECT (( SELECT T2.Value FROM Country AS T1 INNER JOIN Indicators AS T2 ON T1.CountryCode = T2.CountryCode WHERE T1.Alpha2Code = '1A' AND T2.IndicatorName = 'Adolescent fertility rate (births per 1,000 women ages 15-19)' AND T2.Year = 1961 ) - ( SELECT T2.Value FROM Country AS T1 INNER JOIN Indicators AS T2 ON T1.CountryCode = T2.CountryCode WHERE T1.Alpha2Code = '1A' AND T2.IndicatorName = 'Adolescent fertility rate (births per 1,000 women ages 15-19)' AND T2.Year = 1960 )) * 1.0 / ( SELECT SUM(T2.Value) FROM Country AS T1 INNER JOIN Indicators AS T2 ON T1.CountryCode = T2.CountryCode WHERE T1.Alpha2Code = '1A' AND T2.IndicatorName = 'Adolescent fertility rate (births per 1,000 women ages 15-19)' AND T2.Year = 1960 )
[ "What", "is", "the", "percentage", "of", "increase", "of", "the", "indicator", "on", "Adolescent", "fertility", "rate", "from", "1960", "to", "1961", "in", "the", "country", "whose", "Alpha2Code", "is", "1A", "?" ]
[ { "id": 8, "type": "value", "value": "Adolescent fertility rate (births per 1,000 women ages 15-19)" }, { "id": 7, "type": "column", "value": "indicatorname" }, { "id": 4, "type": "column", "value": "countrycode" }, { "id": 2, "type": "table", "value": "indicators" }, { "id": 5, "type": "column", "value": "alpha2code" }, { "id": 1, "type": "table", "value": "country" }, { "id": 3, "type": "column", "value": "value" }, { "id": 9, "type": "column", "value": "year" }, { "id": 10, "type": "value", "value": "1960" }, { "id": 11, "type": "value", "value": "1961" }, { "id": 0, "type": "value", "value": "1.0" }, { "id": 6, "type": "value", "value": "1A" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "B-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "B-VALUE", "O", "B-VALUE", "O", "O", "B-TABLE", "O", "B-COLUMN", "O", "B-VALUE", "O" ]
2,195
synthea
bird:train.json:1484
List out the procedure and medicine prescribed for drug overdose patients.
SELECT DISTINCT T2.DESCRIPTION, T3.DESCRIPTION FROM encounters AS T1 INNER JOIN procedures AS T2 ON T1.PATIENT = T2.PATIENT INNER JOIN medications AS T3 ON T1.PATIENT = T3.PATIENT WHERE T1.REASONDESCRIPTION = 'Drug overdose'
[ "List", "out", "the", "procedure", "and", "medicine", "prescribed", "for", "drug", "overdose", "patients", "." ]
[ { "id": 2, "type": "column", "value": "reasondescription" }, { "id": 3, "type": "value", "value": "Drug overdose" }, { "id": 0, "type": "column", "value": "description" }, { "id": 1, "type": "table", "value": "medications" }, { "id": 4, "type": "table", "value": "encounters" }, { "id": 5, "type": "table", "value": "procedures" }, { "id": 6, "type": "column", "value": "patient" } ]
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[ "O", "O", "O", "B-TABLE", "O", "B-TABLE", "O", "O", "B-VALUE", "I-VALUE", "B-COLUMN", "O" ]
2,196
books
bird:train.json:6088
What is the percentage of books that cost greater than $10 and were ordered by customer Ruthanne Vatini?
SELECT CAST(SUM(CASE WHEN T1.price > 10 THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(*) FROM order_line AS T1 INNER JOIN cust_order AS T2 ON T2.order_id = T1.order_id INNER JOIN customer AS T3 ON T3.customer_id = T2.customer_id WHERE T3.first_name = 'Ruthanne' AND T3.last_name = 'Vatini'
[ "What", "is", "the", "percentage", "of", "books", "that", "cost", "greater", "than", "$", "10", "and", "were", "ordered", "by", "customer", "Ruthanne", "Vatini", "?" ]
[ { "id": 3, "type": "column", "value": "customer_id" }, { "id": 1, "type": "table", "value": "order_line" }, { "id": 2, "type": "table", "value": "cust_order" }, { "id": 4, "type": "column", "value": "first_name" }, { "id": 6, "type": "column", "value": "last_name" }, { "id": 0, "type": "table", "value": "customer" }, { "id": 5, "type": "value", "value": "Ruthanne" }, { "id": 9, "type": "column", "value": "order_id" }, { "id": 7, "type": "value", "value": "Vatini" }, { "id": 12, "type": "column", "value": "price" }, { "id": 8, "type": "value", "value": "100" }, { "id": 13, "type": "value", "value": "10" }, { "id": 10, "type": "value", "value": "0" }, { "id": 11, "type": "value", "value": "1" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-VALUE", "O", "O", "B-COLUMN", "O", "B-TABLE", "B-VALUE", "B-VALUE", "O" ]
2,197
student_loan
bird:train.json:4545
Among all students, calculate the percentage of male students.
SELECT CAST(COUNT(T2.name) AS REAL) * 100 / COUNT(T1.name) FROM person AS T1 LEFT JOIN male AS T2 ON T1.name = T2.name
[ "Among", "all", "students", ",", "calculate", "the", "percentage", "of", "male", "students", "." ]
[ { "id": 0, "type": "table", "value": "person" }, { "id": 1, "type": "table", "value": "male" }, { "id": 2, "type": "column", "value": "name" }, { "id": 3, "type": "value", "value": "100" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "O" ]
2,198
election
spider:train_spider.json:2762
How many distinct delegates are from counties with population larger than 50000?
SELECT count(DISTINCT T2.Delegate) FROM county AS T1 JOIN election AS T2 ON T1.County_id = T2.District WHERE T1.Population > 50000
[ "How", "many", "distinct", "delegates", "are", "from", "counties", "with", "population", "larger", "than", "50000", "?" ]
[ { "id": 2, "type": "column", "value": "population" }, { "id": 5, "type": "column", "value": "county_id" }, { "id": 1, "type": "table", "value": "election" }, { "id": 4, "type": "column", "value": "delegate" }, { "id": 6, "type": "column", "value": "district" }, { "id": 0, "type": "table", "value": "county" }, { "id": 3, "type": "value", "value": "50000" } ]
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[ "O", "O", "B-COLUMN", "B-COLUMN", "O", "O", "B-TABLE", "O", "B-COLUMN", "O", "O", "B-VALUE", "O" ]
2,199
bike_share_1
bird:train.json:9085
What is the ratio of customer to subscriber that making a trip inside Mountain View city?
SELECT CAST(SUM(CASE WHEN T1.subscription_type = 'Customer' THEN 1 ELSE 0 END) AS REAL) * 100 / SUM(CASE WHEN T1.subscription_type = 'Subscriber' THEN 1 ELSE 0 END) FROM trip AS T1 LEFT JOIN station AS T2 ON T2.name = T1.start_station_name WHERE T2.city = 'Mountain View'
[ "What", "is", "the", "ratio", "of", "customer", "to", "subscriber", "that", "making", "a", "trip", "inside", "Mountain", "View", "city", "?" ]
[ { "id": 5, "type": "column", "value": "start_station_name" }, { "id": 9, "type": "column", "value": "subscription_type" }, { "id": 3, "type": "value", "value": "Mountain View" }, { "id": 10, "type": "value", "value": "Subscriber" }, { "id": 11, "type": "value", "value": "Customer" }, { "id": 1, "type": "table", "value": "station" }, { "id": 0, "type": "table", "value": "trip" }, { "id": 2, "type": "column", "value": "city" }, { "id": 4, "type": "column", "value": "name" }, { "id": 6, "type": "value", "value": "100" }, { "id": 7, "type": "value", "value": "0" }, { "id": 8, "type": "value", "value": "1" } ]
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[ "O", "O", "O", "B-TABLE", "O", "B-VALUE", "O", "B-VALUE", "O", "O", "O", "B-TABLE", "O", "B-VALUE", "I-VALUE", "B-COLUMN", "O" ]
2,201
olympics
bird:train.json:4929
How many Olympic Games has London hosted?
SELECT COUNT(T3.id) FROM games_city AS T1 INNER JOIN city AS T2 ON T1.city_id = T2.id INNER JOIN games AS T3 ON T1.games_id = T3.id WHERE T2.city_name = 'London'
[ "How", "many", "Olympic", "Games", "has", "London", "hosted", "?" ]
[ { "id": 4, "type": "table", "value": "games_city" }, { "id": 1, "type": "column", "value": "city_name" }, { "id": 6, "type": "column", "value": "games_id" }, { "id": 7, "type": "column", "value": "city_id" }, { "id": 2, "type": "value", "value": "London" }, { "id": 0, "type": "table", "value": "games" }, { "id": 5, "type": "table", "value": "city" }, { "id": 3, "type": "column", "value": "id" } ]
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[ "O", "O", "O", "B-TABLE", "O", "B-VALUE", "O", "O" ]
2,202
menu
bird:train.json:5504
Who is the sponsor of menu with ID 12463?
SELECT sponsor FROM Menu WHERE id = 12463
[ "Who", "is", "the", "sponsor", "of", "menu", "with", "ID", "12463", "?" ]
[ { "id": 1, "type": "column", "value": "sponsor" }, { "id": 3, "type": "value", "value": "12463" }, { "id": 0, "type": "table", "value": "menu" }, { "id": 2, "type": "column", "value": "id" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "B-TABLE", "O", "B-COLUMN", "B-VALUE", "O" ]
2,203
match_season
spider:train_spider.json:1065
What are the draft pick numbers and draft classes for players who play the Defender position?
SELECT Draft_Pick_Number , Draft_Class FROM match_season WHERE POSITION = "Defender"
[ "What", "are", "the", "draft", "pick", "numbers", "and", "draft", "classes", "for", "players", "who", "play", "the", "Defender", "position", "?" ]
[ { "id": 1, "type": "column", "value": "draft_pick_number" }, { "id": 0, "type": "table", "value": "match_season" }, { "id": 2, "type": "column", "value": "draft_class" }, { "id": 3, "type": "column", "value": "position" }, { "id": 4, "type": "column", "value": "Defender" } ]
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[ "O", "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "O", "B-COLUMN", "I-COLUMN", "O", "O", "O", "O", "O", "B-COLUMN", "B-COLUMN", "O" ]
2,204
institution_sports
bird:test.json:1672
Return the years in which more than 1 institution was founded, as well as the number of institutions founded in each of those.
SELECT Founded , COUNT(*) FROM institution GROUP BY Founded HAVING COUNT(*) > 1
[ "Return", "the", "years", "in", "which", "more", "than", "1", "institution", "was", "founded", ",", "as", "well", "as", "the", "number", "of", "institutions", "founded", "in", "each", "of", "those", "." ]
[ { "id": 0, "type": "table", "value": "institution" }, { "id": 1, "type": "column", "value": "founded" }, { "id": 2, "type": "value", "value": "1" } ]
[ { "entity_id": 0, "token_idxs": [ 8 ] }, { "entity_id": 1, "token_idxs": [ 10 ] }, { "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", "O", "O", "O", "O", "O", "B-VALUE", "B-TABLE", "O", "B-COLUMN", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O" ]
2,205
allergy_1
spider:train_spider.json:489
Show all majors and corresponding number of students.
SELECT major , count(*) FROM Student GROUP BY major
[ "Show", "all", "majors", "and", "corresponding", "number", "of", "students", "." ]
[ { "id": 0, "type": "table", "value": "student" }, { "id": 1, "type": "column", "value": "major" } ]
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[ "O", "O", "B-COLUMN", "O", "O", "O", "O", "B-TABLE", "O" ]
2,206
public_review_platform
bird:train.json:3831
What are the opening hours of business number 53 on Friday?
SELECT T1.closing_time - T1.opening_time AS "opening hours" FROM Business_Hours AS T1 INNER JOIN Days AS T2 ON T1.day_id = T2.day_id WHERE T2.day_of_week LIKE 'Friday' AND T1.business_id = 53
[ "What", "are", "the", "opening", "hours", "of", "business", "number", "53", "on", "Friday", "?" ]
[ { "id": 0, "type": "table", "value": "business_hours" }, { "id": 2, "type": "column", "value": "closing_time" }, { "id": 3, "type": "column", "value": "opening_time" }, { "id": 5, "type": "column", "value": "day_of_week" }, { "id": 7, "type": "column", "value": "business_id" }, { "id": 4, "type": "column", "value": "day_id" }, { "id": 6, "type": "value", "value": "Friday" }, { "id": 1, "type": "table", "value": "days" }, { "id": 8, "type": "value", "value": "53" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "O", "B-COLUMN", "O", "B-VALUE", "O", "B-TABLE", "O" ]
2,207
pilot_1
bird:test.json:1148
Return the average and minimum ages across all pilots.
SELECT avg(age) , min(age) FROM pilotskills
[ "Return", "the", "average", "and", "minimum", "ages", "across", "all", "pilots", "." ]
[ { "id": 0, "type": "table", "value": "pilotskills" }, { "id": 1, "type": "column", "value": "age" } ]
[ { "entity_id": 0, "token_idxs": [ 8 ] }, { "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", "O", "B-TABLE", "O" ]
2,208
station_weather
spider:train_spider.json:3159
Give me the times and numbers of all trains that go to Chennai, ordered by time.
SELECT TIME , train_number FROM train WHERE destination = 'Chennai' ORDER BY TIME
[ "Give", "me", "the", "times", "and", "numbers", "of", "all", "trains", "that", "go", "to", "Chennai", ",", "ordered", "by", "time", "." ]
[ { "id": 2, "type": "column", "value": "train_number" }, { "id": 3, "type": "column", "value": "destination" }, { "id": 4, "type": "value", "value": "Chennai" }, { "id": 0, "type": "table", "value": "train" }, { "id": 1, "type": "column", "value": "time" } ]
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[ "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "B-TABLE", "O", "O", "O", "B-VALUE", "O", "O", "O", "B-COLUMN", "O" ]
2,209
climbing
spider:train_spider.json:1145
What are the countries that have both mountains that are higher than 5600 and lower than 5200?
SELECT Country FROM mountain WHERE Height > 5600 INTERSECT SELECT Country FROM mountain WHERE Height < 5200
[ "What", "are", "the", "countries", "that", "have", "both", "mountains", "that", "are", "higher", "than", "5600", "and", "lower", "than", "5200", "?" ]
[ { "id": 0, "type": "table", "value": "mountain" }, { "id": 1, "type": "column", "value": "country" }, { "id": 2, "type": "column", "value": "height" }, { "id": 3, "type": "value", "value": "5600" }, { "id": 4, "type": "value", "value": "5200" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "O", "O", "B-TABLE", "O", "O", "B-COLUMN", "O", "B-VALUE", "O", "O", "O", "B-VALUE", "O" ]
2,210
protein_institute
spider:train_spider.json:1917
Show institution types, along with the number of institutions and total enrollment for each type.
SELECT TYPE , count(*) , sum(enrollment) FROM institution GROUP BY TYPE
[ "Show", "institution", "types", ",", "along", "with", "the", "number", "of", "institutions", "and", "total", "enrollment", "for", "each", "type", "." ]
[ { "id": 0, "type": "table", "value": "institution" }, { "id": 2, "type": "column", "value": "enrollment" }, { "id": 1, "type": "column", "value": "type" } ]
[ { "entity_id": 0, "token_idxs": [ 1 ] }, { "entity_id": 1, "token_idxs": [ 15 ] }, { "entity_id": 2, "token_idxs": [ 12 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "B-COLUMN", "O" ]
2,211
company_employee
spider:train_spider.json:4100
What is the maximum and minimum market value of companies?
SELECT max(Market_Value_in_Billion) , min(Market_Value_in_Billion) FROM company
[ "What", "is", "the", "maximum", "and", "minimum", "market", "value", "of", "companies", "?" ]
[ { "id": 1, "type": "column", "value": "market_value_in_billion" }, { "id": 0, "type": "table", "value": "company" } ]
[ { "entity_id": 0, "token_idxs": [ 9 ] }, { "entity_id": 1, "token_idxs": [ 6, 7, 8 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "B-TABLE", "O" ]
2,212
csu_1
spider:train_spider.json:2390
Find the campus fee of "San Jose State University" in year 2000.
SELECT t1.campusfee FROM csu_fees AS t1 JOIN campuses AS t2 ON t1.campus = t2.id WHERE t2.campus = "San Jose State University" AND t1.year = 2000
[ "Find", "the", "campus", "fee", "of", "\"", "San", "Jose", "State", "University", "\"", "in", "year", "2000", "." ]
[ { "id": 5, "type": "column", "value": "San Jose State University" }, { "id": 0, "type": "column", "value": "campusfee" }, { "id": 1, "type": "table", "value": "csu_fees" }, { "id": 2, "type": "table", "value": "campuses" }, { "id": 3, "type": "column", "value": "campus" }, { "id": 6, "type": "column", "value": "year" }, { "id": 7, "type": "value", "value": "2000" }, { "id": 4, "type": "column", "value": "id" } ]
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[ "B-COLUMN", "O", "B-COLUMN", "B-COLUMN", "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "I-COLUMN", "O", "O", "B-COLUMN", "B-VALUE", "O" ]
2,213
olympics
bird:train.json:4915
When John Aalberg took part in the 1994 Winter Olympic Game, how old was he?
SELECT T2.age FROM games AS T1 INNER JOIN games_competitor AS T2 ON T1.id = T2.games_id INNER JOIN person AS T3 ON T2.person_id = T3.id WHERE T3.full_name = 'John Aalberg' AND T1.games_name = '1994 Winter'
[ "When", "John", "Aalberg", "took", "part", "in", "the", "1994", "Winter", "Olympic", "Game", ",", "how", "old", "was", "he", "?" ]
[ { "id": 3, "type": "table", "value": "games_competitor" }, { "id": 7, "type": "value", "value": "John Aalberg" }, { "id": 9, "type": "value", "value": "1994 Winter" }, { "id": 8, "type": "column", "value": "games_name" }, { "id": 4, "type": "column", "value": "person_id" }, { "id": 6, "type": "column", "value": "full_name" }, { "id": 10, "type": "column", "value": "games_id" }, { "id": 1, "type": "table", "value": "person" }, { "id": 2, "type": "table", "value": "games" }, { "id": 0, "type": "column", "value": "age" }, { "id": 5, "type": "column", "value": "id" } ]
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[ "O", "B-VALUE", "I-VALUE", "O", "O", "O", "O", "B-VALUE", "I-VALUE", "O", "B-TABLE", "O", "O", "O", "O", "O", "O" ]
2,214
riding_club
spider:train_spider.json:1725
Show different occupations along with the number of players in each occupation.
SELECT Occupation , COUNT(*) FROM player GROUP BY Occupation
[ "Show", "different", "occupations", "along", "with", "the", "number", "of", "players", "in", "each", "occupation", "." ]
[ { "id": 1, "type": "column", "value": "occupation" }, { "id": 0, "type": "table", "value": "player" } ]
[ { "entity_id": 0, "token_idxs": [ 8 ] }, { "entity_id": 1, "token_idxs": [ 11 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "B-COLUMN", "O" ]
2,215
cinema
spider:train_spider.json:1937
Find the distinct locations that has a cinema.
SELECT DISTINCT LOCATION FROM cinema
[ "Find", "the", "distinct", "locations", "that", "has", "a", "cinema", "." ]
[ { "id": 1, "type": "column", "value": "location" }, { "id": 0, "type": "table", "value": "cinema" } ]
[ { "entity_id": 0, "token_idxs": [ 7 ] }, { "entity_id": 1, "token_idxs": [ 3 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-COLUMN", "O", "O", "O", "B-TABLE", "O" ]
2,216
customer_complaints
spider:train_spider.json:5805
Count the number of different complaint type codes.
SELECT count(DISTINCT complaint_type_code) FROM complaints
[ "Count", "the", "number", "of", "different", "complaint", "type", "codes", "." ]
[ { "id": 1, "type": "column", "value": "complaint_type_code" }, { "id": 0, "type": "table", "value": "complaints" } ]
[ { "entity_id": 0, "token_idxs": [ 5 ] }, { "entity_id": 1, "token_idxs": [ 6, 7 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "B-TABLE", "B-COLUMN", "I-COLUMN", "O" ]
2,217
hockey
bird:train.json:7682
How many years did player Id "healygl01" play?
SELECT COUNT(year) FROM Goalies WHERE playerID = 'healygl01'
[ "How", "many", "years", "did", "player", "I", "d", "\"", "healygl01", "\"", "play", "?" ]
[ { "id": 2, "type": "value", "value": "healygl01" }, { "id": 1, "type": "column", "value": "playerid" }, { "id": 0, "type": "table", "value": "goalies" }, { "id": 3, "type": "column", "value": "year" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 4, 5, 6 ] }, { "entity_id": 2, "token_idxs": [ 8 ] }, { "entity_id": 3, "token_idxs": [ 2 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-COLUMN", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "O", "B-VALUE", "O", "O", "O" ]
2,218
toxicology
bird:dev.json:329
Which carcinogenic molecule have the highest number of atoms consisted in it?
SELECT T.molecule_id FROM ( SELECT T2.molecule_id, COUNT(T1.atom_id) FROM atom AS T1 INNER JOIN molecule AS T2 ON T1.molecule_id = T2.molecule_id WHERE T2.label = '+' GROUP BY T2.molecule_id ORDER BY COUNT(T1.atom_id) DESC LIMIT 1 ) t
[ "Which", "carcinogenic", "molecule", "have", "the", "highest", "number", "of", "atoms", "consisted", "in", "it", "?" ]
[ { "id": 0, "type": "column", "value": "molecule_id" }, { "id": 2, "type": "table", "value": "molecule" }, { "id": 5, "type": "column", "value": "atom_id" }, { "id": 3, "type": "column", "value": "label" }, { "id": 1, "type": "table", "value": "atom" }, { "id": 4, "type": "value", "value": "+" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 8 ] }, { "entity_id": 2, "token_idxs": [ 2 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "O" ]
2,219
movies_4
bird:train.json:468
What is the title of the highest-budget film to date? Please include the revenue and name the country.
SELECT T1.title, T1.revenue, T3.COUNTry_name FROM movie AS T1 INNER JOIN production_COUNTry AS T2 ON T1.movie_id = T2.movie_id INNER JOIN COUNTry AS T3 ON T2.COUNTry_id = T3.COUNTry_id ORDER BY T1.budget DESC LIMIT 1
[ "What", "is", "the", "title", "of", "the", "highest", "-", "budget", "film", "to", "date", "?", "Please", "include", "the", "revenue", "and", "name", "the", "country", "." ]
[ { "id": 6, "type": "table", "value": "production_country" }, { "id": 2, "type": "column", "value": "country_name" }, { "id": 7, "type": "column", "value": "country_id" }, { "id": 8, "type": "column", "value": "movie_id" }, { "id": 1, "type": "column", "value": "revenue" }, { "id": 3, "type": "table", "value": "country" }, { "id": 4, "type": "column", "value": "budget" }, { "id": 0, "type": "column", "value": "title" }, { "id": 5, "type": "table", "value": "movie" } ]
[ { "entity_id": 0, "token_idxs": [ 3 ] }, { "entity_id": 1, "token_idxs": [ 16 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 20 ] }, { "entity_id": 4, "token_idxs": [ 8 ] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-COLUMN", "O", "O", "O", "O", "B-COLUMN", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "O", "B-TABLE", "O" ]
2,220
movie_platform
bird:train.json:27
What is the average rating score of the movie "When Will I Be Loved" and who was its director?
SELECT AVG(T1.rating_score), T2.director_name FROM ratings AS T1 INNER JOIN movies AS T2 ON T1.movie_id = T2.movie_id WHERE T2.movie_title = 'When Will I Be Loved'
[ "What", "is", "the", "average", "rating", "score", "of", "the", "movie", "\"", "When", "Will", "I", "Be", "Loved", "\"", "and", "who", "was", "its", "director", "?" ]
[ { "id": 4, "type": "value", "value": "When Will I Be Loved" }, { "id": 0, "type": "column", "value": "director_name" }, { "id": 5, "type": "column", "value": "rating_score" }, { "id": 3, "type": "column", "value": "movie_title" }, { "id": 6, "type": "column", "value": "movie_id" }, { "id": 1, "type": "table", "value": "ratings" }, { "id": 2, "type": "table", "value": "movies" } ]
[ { "entity_id": 0, "token_idxs": [ 20 ] }, { "entity_id": 1, "token_idxs": [ 4 ] }, { "entity_id": 2, "token_idxs": [ 8 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [ 10, 11, 12, 13, 14 ] }, { "entity_id": 5, "token_idxs": [ 5 ] }, { "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", "B-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "O", "O", "O", "O", "O", "B-COLUMN", "O" ]
2,221
cre_Drama_Workshop_Groups
spider:train_spider.json:5094
Show all the planned delivery dates and actual delivery dates of bookings.
SELECT Planned_Delivery_Date , Actual_Delivery_Date FROM BOOKINGS
[ "Show", "all", "the", "planned", "delivery", "dates", "and", "actual", "delivery", "dates", "of", "bookings", "." ]
[ { "id": 1, "type": "column", "value": "planned_delivery_date" }, { "id": 2, "type": "column", "value": "actual_delivery_date" }, { "id": 0, "type": "table", "value": "bookings" } ]
[ { "entity_id": 0, "token_idxs": [ 11 ] }, { "entity_id": 1, "token_idxs": [ 3, 4, 5 ] }, { "entity_id": 2, "token_idxs": [ 7, 8, 9 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "O", "B-TABLE", "O" ]
2,222
network_2
spider:train_spider.json:4450
Find the person who has exactly one friend.
SELECT name FROM PersonFriend GROUP BY name HAVING count(*) = 1
[ "Find", "the", "person", "who", "has", "exactly", "one", "friend", "." ]
[ { "id": 0, "type": "table", "value": "personfriend" }, { "id": 1, "type": "column", "value": "name" }, { "id": 2, "type": "value", "value": "1" } ]
[ { "entity_id": 0, "token_idxs": [ 6, 7 ] }, { "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", "B-TABLE", "I-TABLE", "O" ]
2,224
store_1
spider:train_spider.json:547
List the top 10 customers by total gross sales. List customers' first and last name and total gross sales.
SELECT T1.first_name , T1.last_name , SUM(T2.total) FROM customers AS T1 JOIN invoices AS T2 ON T2.customer_id = T1.id GROUP BY T1.id ORDER BY SUM(T2.total) DESC LIMIT 10;
[ "List", "the", "top", "10", "customers", "by", "total", "gross", "sales", ".", "List", "customers", "'", "first", "and", "last", "name", "and", "total", "gross", "sales", "." ]
[ { "id": 6, "type": "column", "value": "customer_id" }, { "id": 1, "type": "column", "value": "first_name" }, { "id": 2, "type": "column", "value": "last_name" }, { "id": 3, "type": "table", "value": "customers" }, { "id": 4, "type": "table", "value": "invoices" }, { "id": 5, "type": "column", "value": "total" }, { "id": 0, "type": "column", "value": "id" } ]
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[ "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "B-COLUMN", "I-COLUMN", "O", "B-COLUMN", "O", "O", "O" ]
2,225
social_media
bird:train.json:805
What is the gender of the user who tweeted `tw-715909161071091712`?
SELECT T2.Gender FROM twitter AS T1 INNER JOIN user AS T2 ON T1.UserID = T2.UserID WHERE T1.TweetID = 'tw-715909161071091712'
[ "What", "is", "the", "gender", "of", "the", "user", "who", "tweeted", "`", "tw-715909161071091712", "`", "?" ]
[ { "id": 3, "type": "value", "value": "tw-715909161071091712" }, { "id": 1, "type": "table", "value": "twitter" }, { "id": 2, "type": "column", "value": "tweetid" }, { "id": 0, "type": "column", "value": "gender" }, { "id": 4, "type": "column", "value": "userid" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "O", "B-COLUMN", "O", "B-COLUMN", "O", "B-VALUE", "O", "O" ]
2,226
customers_and_addresses
spider:train_spider.json:6134
What are all the addresses in East Julianaside, Texas or in Gleasonmouth, Arizona.
SELECT address_content FROM addresses WHERE city = "East Julianaside" AND state_province_county = "Texas" UNION SELECT address_content FROM addresses WHERE city = "Gleasonmouth" AND state_province_county = "Arizona"
[ "What", "are", "all", "the", "addresses", "in", "East", "Julianaside", ",", "Texas", "or", "in", "Gleasonmouth", ",", "Arizona", "." ]
[ { "id": 4, "type": "column", "value": "state_province_county" }, { "id": 3, "type": "column", "value": "East Julianaside" }, { "id": 1, "type": "column", "value": "address_content" }, { "id": 6, "type": "column", "value": "Gleasonmouth" }, { "id": 0, "type": "table", "value": "addresses" }, { "id": 7, "type": "column", "value": "Arizona" }, { "id": 5, "type": "column", "value": "Texas" }, { "id": 2, "type": "column", "value": "city" } ]
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[ "O", "O", "O", "O", "B-TABLE", "O", "B-COLUMN", "I-COLUMN", "O", "B-COLUMN", "O", "O", "B-COLUMN", "O", "B-COLUMN", "O" ]
2,227
movie_3
bird:train.json:9233
List the inventory ID of the film titled "African Egg".
SELECT T2.inventory_id FROM film AS T1 INNER JOIN inventory AS T2 ON T1.film_id = T2.film_id WHERE T1.title = 'African Egg'
[ "List", "the", "inventory", "ID", "of", "the", "film", "titled", "\"", "African", "Egg", "\"", "." ]
[ { "id": 0, "type": "column", "value": "inventory_id" }, { "id": 4, "type": "value", "value": "African Egg" }, { "id": 2, "type": "table", "value": "inventory" }, { "id": 5, "type": "column", "value": "film_id" }, { "id": 3, "type": "column", "value": "title" }, { "id": 1, "type": "table", "value": "film" } ]
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[ "O", "O", "B-TABLE", "B-COLUMN", "O", "O", "B-TABLE", "B-COLUMN", "O", "B-VALUE", "I-VALUE", "O", "O" ]
2,228
food_inspection
bird:train.json:8851
What is the owner's name of the of the business that violates 103156 on June 12, 2014?
SELECT DISTINCT T2.owner_name FROM violations AS T1 INNER JOIN businesses AS T2 ON T1.business_id = T2.business_id WHERE T1.violation_type_id = 103156 AND T1.`date` = '2014-06-12'
[ "What", "is", "the", "owner", "'s", "name", "of", "the", "of", "the", "business", "that", "violates", "103156", "on", "June", "12", ",", "2014", "?" ]
[ { "id": 4, "type": "column", "value": "violation_type_id" }, { "id": 3, "type": "column", "value": "business_id" }, { "id": 0, "type": "column", "value": "owner_name" }, { "id": 1, "type": "table", "value": "violations" }, { "id": 2, "type": "table", "value": "businesses" }, { "id": 7, "type": "value", "value": "2014-06-12" }, { "id": 5, "type": "value", "value": "103156" }, { "id": 6, "type": "column", "value": "date" } ]
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[ "O", "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "O", "O", "O", "O", "B-TABLE", "O", "B-TABLE", "B-VALUE", "O", "O", "O", "O", "O", "O" ]
2,229
ice_hockey_draft
bird:train.json:6920
How many players weigh more than 90 kg?
SELECT COUNT(T1.ELITEID) FROM PlayerInfo AS T1 INNER JOIN weight_info AS T2 ON T1.weight = T2.weight_id WHERE T2.weight_in_kg > 90
[ "How", "many", "players", "weigh", "more", "than", "90", "kg", "?" ]
[ { "id": 2, "type": "column", "value": "weight_in_kg" }, { "id": 1, "type": "table", "value": "weight_info" }, { "id": 0, "type": "table", "value": "playerinfo" }, { "id": 6, "type": "column", "value": "weight_id" }, { "id": 4, "type": "column", "value": "eliteid" }, { "id": 5, "type": "column", "value": "weight" }, { "id": 3, "type": "value", "value": "90" } ]
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[ "O", "O", "B-TABLE", "B-COLUMN", "O", "O", "B-VALUE", "O", "O" ]
2,230
bike_1
spider:train_spider.json:191
For each station, find its latitude and the minimum duration of trips that ended at the station.
SELECT T1.name , T1.lat , min(T2.duration) FROM station AS T1 JOIN trip AS T2 ON T1.id = T2.end_station_id GROUP BY T2.end_station_id
[ "For", "each", "station", ",", "find", "its", "latitude", "and", "the", "minimum", "duration", "of", "trips", "that", "ended", "at", "the", "station", "." ]
[ { "id": 0, "type": "column", "value": "end_station_id" }, { "id": 5, "type": "column", "value": "duration" }, { "id": 3, "type": "table", "value": "station" }, { "id": 1, "type": "column", "value": "name" }, { "id": 4, "type": "table", "value": "trip" }, { "id": 2, "type": "column", "value": "lat" }, { "id": 6, "type": "column", "value": "id" } ]
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[ "O", "O", "B-COLUMN", "I-COLUMN", "B-COLUMN", "O", "O", "O", "O", "O", "B-COLUMN", "O", "B-TABLE", "O", "O", "B-COLUMN", "O", "B-TABLE", "O" ]
2,231
retail_world
bird:train.json:6550
What products were ordered by the customer ID "WILMK" which were required on 3/26/1998?
SELECT T3.ProductName FROM Orders AS T1 INNER JOIN `Order Details` AS T2 ON T1.OrderID = T2.OrderID INNER JOIN Products AS T3 ON T2.ProductID = T3.ProductID WHERE T1.RequiredDate LIKE '1998-03-26%' AND T1.CustomerID = 'WILMK'
[ "What", "products", "were", "ordered", "by", "the", "customer", "ID", "\"", "WILMK", "\"", "which", "were", "required", "on", "3/26/1998", "?" ]
[ { "id": 3, "type": "table", "value": "Order Details" }, { "id": 5, "type": "column", "value": "requireddate" }, { "id": 0, "type": "column", "value": "productname" }, { "id": 6, "type": "value", "value": "1998-03-26%" }, { "id": 7, "type": "column", "value": "customerid" }, { "id": 4, "type": "column", "value": "productid" }, { "id": 1, "type": "table", "value": "products" }, { "id": 9, "type": "column", "value": "orderid" }, { "id": 2, "type": "table", "value": "orders" }, { "id": 8, "type": "value", "value": "WILMK" } ]
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[ "O", "B-TABLE", "O", "B-TABLE", "O", "O", "B-COLUMN", "I-COLUMN", "O", "B-VALUE", "O", "O", "O", "B-COLUMN", "O", "O", "O" ]
2,232
talkingdata
bird:train.json:1143
For the device with an event occurring on 2016/5/1 at 0:55:25, what is the gender of its user?
SELECT T1.gender FROM gender_age AS T1 INNER JOIN events AS T2 ON T1.device_id = T2.device_id WHERE T2.timestamp = '2016-05-01 00:55:25'
[ "For", "the", "device", "with", "an", "event", "occurring", "on", "2016/5/1", "at", "0:55:25", ",", "what", "is", "the", "gender", "of", "its", "user", "?" ]
[ { "id": 4, "type": "value", "value": "2016-05-01 00:55:25" }, { "id": 1, "type": "table", "value": "gender_age" }, { "id": 3, "type": "column", "value": "timestamp" }, { "id": 5, "type": "column", "value": "device_id" }, { "id": 0, "type": "column", "value": "gender" }, { "id": 2, "type": "table", "value": "events" } ]
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[ "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "B-VALUE", "I-VALUE", "I-VALUE", "O", "O", "O", "O", "B-COLUMN", "O", "O", "O", "O" ]
2,233
formula_1
bird:dev.json:847
What is the surname of the driver with the best lap time in race number 19 in the second qualifying period?
SELECT T2.surname FROM qualifying AS T1 INNER JOIN drivers AS T2 ON T2.driverId = T1.driverId WHERE T1.raceId = 19 ORDER BY T1.q2 ASC LIMIT 1
[ "What", "is", "the", "surname", "of", "the", "driver", "with", "the", "best", "lap", "time", "in", "race", "number", "19", "in", "the", "second", "qualifying", "period", "?" ]
[ { "id": 1, "type": "table", "value": "qualifying" }, { "id": 6, "type": "column", "value": "driverid" }, { "id": 0, "type": "column", "value": "surname" }, { "id": 2, "type": "table", "value": "drivers" }, { "id": 3, "type": "column", "value": "raceid" }, { "id": 4, "type": "value", "value": "19" }, { "id": 5, "type": "column", "value": "q2" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "B-VALUE", "O", "O", "O", "B-TABLE", "O", "O" ]
2,234
regional_sales
bird:train.json:2737
What is the average household income of Glendale?
SELECT AVG(`Household Income`) FROM `Store Locations` WHERE `City Name` = 'Glendale'
[ "What", "is", "the", "average", "household", "income", "of", "Glendale", "?" ]
[ { "id": 3, "type": "column", "value": "Household Income" }, { "id": 0, "type": "table", "value": "Store Locations" }, { "id": 1, "type": "column", "value": "City Name" }, { "id": 2, "type": "value", "value": "Glendale" } ]
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[ "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "B-VALUE", "O" ]
2,235
college_1
spider:train_spider.json:3251
How many students are enrolled in the class taught by some professor from the accounting department?
SELECT count(*) FROM CLASS AS T1 JOIN enroll AS T2 ON T1.class_code = T2.class_code JOIN course AS T3 ON T1.crs_code = T3.crs_code JOIN department AS T4 ON T3.dept_code = T4.dept_code WHERE T4.dept_name = 'Accounting'
[ "How", "many", "students", "are", "enrolled", "in", "the", "class", "taught", "by", "some", "professor", "from", "the", "accounting", "department", "?" ]
[ { "id": 0, "type": "table", "value": "department" }, { "id": 2, "type": "value", "value": "Accounting" }, { "id": 8, "type": "column", "value": "class_code" }, { "id": 1, "type": "column", "value": "dept_name" }, { "id": 4, "type": "column", "value": "dept_code" }, { "id": 7, "type": "column", "value": "crs_code" }, { "id": 3, "type": "table", "value": "course" }, { "id": 6, "type": "table", "value": "enroll" }, { "id": 5, "type": "table", "value": "class" } ]
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[ "O", "O", "O", "O", "B-TABLE", "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "B-VALUE", "B-COLUMN", "O" ]
2,236
cookbook
bird:train.json:8915
Provide the title and total time of the recipe which can be made with only lima beans.
SELECT T1.title, T1.prep_min + T1.cook_min + T1.stnd_min FROM Recipe AS T1 INNER JOIN Quantity AS T2 ON T1.recipe_id = T2.recipe_id INNER JOIN Ingredient AS T3 ON T3.ingredient_id = T2.ingredient_id WHERE T3.name = 'lima beans'
[ "Provide", "the", "title", "and", "total", "time", "of", "the", "recipe", "which", "can", "be", "made", "with", "only", "lima", "beans", "." ]
[ { "id": 7, "type": "column", "value": "ingredient_id" }, { "id": 1, "type": "table", "value": "ingredient" }, { "id": 3, "type": "value", "value": "lima beans" }, { "id": 10, "type": "column", "value": "recipe_id" }, { "id": 4, "type": "column", "value": "stnd_min" }, { "id": 6, "type": "table", "value": "quantity" }, { "id": 8, "type": "column", "value": "prep_min" }, { "id": 9, "type": "column", "value": "cook_min" }, { "id": 5, "type": "table", "value": "recipe" }, { "id": 0, "type": "column", "value": "title" }, { "id": 2, "type": "column", "value": "name" } ]
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[ "O", "O", "B-COLUMN", "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "B-VALUE", "I-VALUE", "O" ]
2,237
works_cycles
bird:train.json:7261
Among the vendors with maximum orders betweeen 500 to 750, which vendor has the 10th highest profit on net?
SELECT T2.Name FROM ProductVendor AS T1 INNER JOIN Vendor AS T2 ON T1.BusinessEntityID = T2.BusinessEntityID WHERE T1.MaxOrderQty BETWEEN 500 AND 750 ORDER BY T1.LastReceiptCost - T1.StandardPrice DESC LIMIT 9, 1
[ "Among", "the", "vendors", "with", "maximum", "orders", "betweeen", "500", "to", "750", ",", "which", "vendor", "has", "the", "10th", "highest", "profit", "on", "net", "?" ]
[ { "id": 6, "type": "column", "value": "businessentityid" }, { "id": 7, "type": "column", "value": "lastreceiptcost" }, { "id": 1, "type": "table", "value": "productvendor" }, { "id": 8, "type": "column", "value": "standardprice" }, { "id": 3, "type": "column", "value": "maxorderqty" }, { "id": 2, "type": "table", "value": "vendor" }, { "id": 0, "type": "column", "value": "name" }, { "id": 4, "type": "value", "value": "500" }, { "id": 5, "type": "value", "value": "750" } ]
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[ "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "B-VALUE", "O", "B-VALUE", "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "O", "O" ]
2,238
flight_1
spider:train_spider.json:344
How many aircrafts exist in the database?
SELECT count(*) FROM Aircraft
[ "How", "many", "aircrafts", "exist", "in", "the", "database", "?" ]
[ { "id": 0, "type": "table", "value": "aircraft" } ]
[ { "entity_id": 0, "token_idxs": [ 2 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-TABLE", "O", "O", "O", "O", "O" ]
2,239
sales_in_weather
bird:train.json:8198
How many days did the show fell more than 5 inches?
SELECT COUNT(DISTINCT `date`) FROM weather WHERE snowfall > 5
[ "How", "many", "days", "did", "the", "show", "fell", "more", "than", "5", "inches", "?" ]
[ { "id": 1, "type": "column", "value": "snowfall" }, { "id": 0, "type": "table", "value": "weather" }, { "id": 3, "type": "column", "value": "date" }, { "id": 2, "type": "value", "value": "5" } ]
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[ "O", "O", "O", "O", "B-TABLE", "B-COLUMN", "I-COLUMN", "O", "O", "B-VALUE", "O", "O" ]
2,240
store_1
spider:train_spider.json:545
Find out the top 10 customers by total number of orders. List customers' first and last name and the number of total orders.
SELECT T1.first_name , T1.last_name , COUNT(*) FROM customers AS T1 JOIN invoices AS T2 ON T2.customer_id = T1.id GROUP BY T1.id ORDER BY COUNT(*) DESC LIMIT 10;
[ "Find", "out", "the", "top", "10", "customers", "by", "total", "number", "of", "orders", ".", "List", "customers", "'", "first", "and", "last", "name", "and", "the", "number", "of", "total", "orders", "." ]
[ { "id": 5, "type": "column", "value": "customer_id" }, { "id": 1, "type": "column", "value": "first_name" }, { "id": 2, "type": "column", "value": "last_name" }, { "id": 3, "type": "table", "value": "customers" }, { "id": 4, "type": "table", "value": "invoices" }, { "id": 0, "type": "column", "value": "id" } ]
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[ "B-COLUMN", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "B-COLUMN", "O", "B-COLUMN", "I-COLUMN", "O", "O", "O", "O", "O", "O", "O" ]
2,241
customers_and_invoices
spider:train_spider.json:1567
What is the customer id, first and last name with most number of accounts.
SELECT T1.customer_id , T2.customer_first_name , T2.customer_last_name FROM Accounts AS T1 JOIN Customers AS T2 ON T1.customer_id = T2.customer_id GROUP BY T1.customer_id ORDER BY count(*) DESC LIMIT 1
[ "What", "is", "the", "customer", "i", "d", ",", "first", "and", "last", "name", "with", "most", "number", "of", "accounts", "." ]
[ { "id": 1, "type": "column", "value": "customer_first_name" }, { "id": 2, "type": "column", "value": "customer_last_name" }, { "id": 0, "type": "column", "value": "customer_id" }, { "id": 4, "type": "table", "value": "customers" }, { "id": 3, "type": "table", "value": "accounts" } ]
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[ "O", "O", "O", "B-TABLE", "B-COLUMN", "I-COLUMN", "B-COLUMN", "I-COLUMN", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "O" ]
2,242
mondial_geo
bird:train.json:8379
Which country was the source of Pjandsh River? Give the full name of the country.
SELECT T1.Name FROM country AS T1 INNER JOIN located AS T2 ON T1.Code = T2.Country WHERE T2.River = 'Pjandsh'
[ "Which", "country", "was", "the", "source", "of", "Pjandsh", "River", "?", "Give", "the", "full", "name", "of", "the", "country", "." ]
[ { "id": 1, "type": "table", "value": "country" }, { "id": 2, "type": "table", "value": "located" }, { "id": 4, "type": "value", "value": "Pjandsh" }, { "id": 6, "type": "column", "value": "country" }, { "id": 3, "type": "column", "value": "river" }, { "id": 0, "type": "column", "value": "name" }, { "id": 5, "type": "column", "value": "code" } ]
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[ "O", "B-COLUMN", "O", "O", "O", "O", "B-VALUE", "B-COLUMN", "O", "O", "O", "O", "B-COLUMN", "O", "O", "O", "O" ]
2,243
superhero
bird:dev.json:776
Provide the hero name and race of Charles Chandler.
SELECT T1.superhero_name, T2.race FROM superhero AS T1 INNER JOIN race AS T2 ON T1.race_id = T2.id WHERE T1.full_name = 'Charles Chandler'
[ "Provide", "the", "hero", "name", "and", "race", "of", "Charles", "Chandler", "." ]
[ { "id": 5, "type": "value", "value": "Charles Chandler" }, { "id": 0, "type": "column", "value": "superhero_name" }, { "id": 2, "type": "table", "value": "superhero" }, { "id": 4, "type": "column", "value": "full_name" }, { "id": 6, "type": "column", "value": "race_id" }, { "id": 1, "type": "column", "value": "race" }, { "id": 3, "type": "table", "value": "race" }, { "id": 7, "type": "column", "value": "id" } ]
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[ "O", "O", "B-TABLE", "B-COLUMN", "O", "B-TABLE", "O", "B-VALUE", "I-VALUE", "O" ]
2,244
music_4
spider:train_spider.json:6155
What are the category of music festivals with result "Awarded"?
SELECT Category FROM music_festival WHERE RESULT = "Awarded"
[ "What", "are", "the", "category", "of", "music", "festivals", "with", "result", "\"", "Awarded", "\"", "?" ]
[ { "id": 0, "type": "table", "value": "music_festival" }, { "id": 1, "type": "column", "value": "category" }, { "id": 3, "type": "column", "value": "Awarded" }, { "id": 2, "type": "column", "value": "result" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "B-TABLE", "I-TABLE", "O", "B-COLUMN", "O", "B-COLUMN", "O", "O" ]
2,245
document_management
spider:train_spider.json:4535
Find the average access count of documents with the least popular structure.
SELECT avg(access_count) FROM documents GROUP BY document_structure_code ORDER BY count(*) ASC LIMIT 1
[ "Find", "the", "average", "access", "count", "of", "documents", "with", "the", "least", "popular", "structure", "." ]
[ { "id": 1, "type": "column", "value": "document_structure_code" }, { "id": 2, "type": "column", "value": "access_count" }, { "id": 0, "type": "table", "value": "documents" } ]
[ { "entity_id": 0, "token_idxs": [ 6 ] }, { "entity_id": 1, "token_idxs": [ 7, 8 ] }, { "entity_id": 2, "token_idxs": [ 3, 4 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "B-TABLE", "B-COLUMN", "I-COLUMN", "O", "O", "O", "O" ]
2,246
works_cycles
bird:train.json:7317
Other than the Chief Executive Officer, who is the employee who has the highest payrate? State the rate.
SELECT T2.FirstName, T2.LastName FROM EmployeePayHistory AS T1 INNER JOIN Person AS T2 ON T1.BusinessEntityID = T2.BusinessEntityID INNER JOIN Employee AS T3 ON T2.BusinessEntityID = T3.BusinessEntityID WHERE T3.JobTitle NOT LIKE 'Chief Executive Officer' ORDER BY T1.Rate DESC LIMIT 1
[ "Other", "than", "the", "Chief", "Executive", "Officer", ",", "who", "is", "the", "employee", "who", "has", "the", "highest", "payrate", "?", "State", "the", "rate", "." ]
[ { "id": 4, "type": "value", "value": "Chief Executive Officer" }, { "id": 6, "type": "table", "value": "employeepayhistory" }, { "id": 8, "type": "column", "value": "businessentityid" }, { "id": 0, "type": "column", "value": "firstname" }, { "id": 1, "type": "column", "value": "lastname" }, { "id": 2, "type": "table", "value": "employee" }, { "id": 3, "type": "column", "value": "jobtitle" }, { "id": 7, "type": "table", "value": "person" }, { "id": 5, "type": "column", "value": "rate" } ]
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[ "O", "O", "O", "B-VALUE", "I-VALUE", "I-VALUE", "O", "O", "O", "O", "B-TABLE", "B-TABLE", "O", "O", "O", "O", "O", "B-COLUMN", "O", "B-COLUMN", "O" ]
2,247
railway
spider:train_spider.json:5640
Show the names of trains and locations of railways they are in.
SELECT T2.Name , T1.Location FROM railway AS T1 JOIN train AS T2 ON T1.Railway_ID = T2.Railway_ID
[ "Show", "the", "names", "of", "trains", "and", "locations", "of", "railways", "they", "are", "in", "." ]
[ { "id": 4, "type": "column", "value": "railway_id" }, { "id": 1, "type": "column", "value": "location" }, { "id": 2, "type": "table", "value": "railway" }, { "id": 3, "type": "table", "value": "train" }, { "id": 0, "type": "column", "value": "name" } ]
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[ "O", "O", "B-COLUMN", "O", "B-TABLE", "O", "B-COLUMN", "O", "B-TABLE", "O", "O", "O", "O" ]
2,248
college_completion
bird:train.json:3742
List the race of institutions in Alabama with number of students greater than the 90% of average number of students of all institutions?
SELECT DISTINCT T2.race FROM institution_details AS T1 INNER JOIN institution_grads AS T2 ON T2.unitid = T1.unitid WHERE T1.student_count > ( SELECT AVG(T1.student_count) * 0.9 FROM institution_details AS T1 INNER JOIN institution_grads AS T2 ON T2.unitid = T1.unitid WHERE T1.state = 'Alabama' ) AND T1.state = 'Alabama'
[ "List", "the", "race", "of", "institutions", "in", "Alabama", "with", "number", "of", "students", "greater", "than", "the", "90", "%", "of", "average", "number", "of", "students", "of", "all", "institutions", "?" ]
[ { "id": 1, "type": "table", "value": "institution_details" }, { "id": 2, "type": "table", "value": "institution_grads" }, { "id": 4, "type": "column", "value": "student_count" }, { "id": 6, "type": "value", "value": "Alabama" }, { "id": 3, "type": "column", "value": "unitid" }, { "id": 5, "type": "column", "value": "state" }, { "id": 0, "type": "column", "value": "race" }, { "id": 7, "type": "value", "value": "0.9" } ]
[ { "entity_id": 0, "token_idxs": [ 2 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 4 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [ 10 ] }, { "entity_id": 5, "token_idxs": [ 0, 1 ] }, { "entity_id": 6, "token_idxs": [ 6 ] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "B-COLUMN", "I-COLUMN", "B-COLUMN", "O", "B-TABLE", "O", "B-VALUE", "O", "O", "O", "B-COLUMN", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O" ]
2,249
mondial_geo
bird:train.json:8377
What is the main spoken language in MNE?
SELECT Name FROM language WHERE Country = 'MNE' ORDER BY Percentage DESC LIMIT 1
[ "What", "is", "the", "main", "spoken", "language", "in", "MNE", "?" ]
[ { "id": 4, "type": "column", "value": "percentage" }, { "id": 0, "type": "table", "value": "language" }, { "id": 2, "type": "column", "value": "country" }, { "id": 1, "type": "column", "value": "name" }, { "id": 3, "type": "value", "value": "MNE" } ]
[ { "entity_id": 0, "token_idxs": [ 5 ] }, { "entity_id": 1, "token_idxs": [ 6 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 7 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "B-TABLE", "B-COLUMN", "B-VALUE", "O" ]
2,250
movie_platform
bird:train.json:83
How much higher is the average rating score of the movie "Innocence Unprotected" than the movie "When Will I Be Loved"?
SELECT SUM(CASE WHEN T2.movie_title = 'Innocence Unprotected' THEN T1.rating_score ELSE 0 END) / SUM(CASE WHEN T2.movie_title = 'Innocence Unprotected' THEN 1 ELSE 0 END) - SUM(CASE WHEN T2.movie_title = 'When Will I Be Loved' THEN T1.rating_score ELSE 0 END) / SUM(CASE WHEN T2.movie_title = 'When Will I Be Loved' THEN 1 ELSE 0 END) FROM ratings AS T1 INNER JOIN movies AS T2 ON T1.movie_id = T2.movie_id
[ "How", "much", "higher", "is", "the", "average", "rating", "score", "of", "the", "movie", "\"", "Innocence", "Unprotected", "\"", "than", "the", "movie", "\"", "When", "Will", "I", "Be", "Loved", "\"", "?" ]
[ { "id": 7, "type": "value", "value": "Innocence Unprotected" }, { "id": 8, "type": "value", "value": "When Will I Be Loved" }, { "id": 4, "type": "column", "value": "rating_score" }, { "id": 6, "type": "column", "value": "movie_title" }, { "id": 2, "type": "column", "value": "movie_id" }, { "id": 0, "type": "table", "value": "ratings" }, { "id": 1, "type": "table", "value": "movies" }, { "id": 3, "type": "value", "value": "0" }, { "id": 5, "type": "value", "value": "1" } ]
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[ "O", "O", "O", "O", "O", "O", "B-TABLE", "B-COLUMN", "O", "O", "B-TABLE", "O", "B-VALUE", "I-VALUE", "O", "O", "O", "B-COLUMN", "O", "B-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "O", "O" ]
2,251
cre_Doc_Control_Systems
spider:train_spider.json:2109
How many employees does each role have? List role description, id and number of employees.
SELECT T1.role_description , T2.role_code , count(*) FROM ROLES AS T1 JOIN Employees AS T2 ON T1.role_code = T2.role_code GROUP BY T2.role_code;
[ "How", "many", "employees", "does", "each", "role", "have", "?", "List", "role", "description", ",", "i", "d", "and", "number", "of", "employees", "." ]
[ { "id": 1, "type": "column", "value": "role_description" }, { "id": 0, "type": "column", "value": "role_code" }, { "id": 3, "type": "table", "value": "employees" }, { "id": 2, "type": "table", "value": "roles" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 10 ] }, { "entity_id": 2, "token_idxs": [ 9 ] }, { "entity_id": 3, "token_idxs": [ 17 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "B-COLUMN", "O", "O", "O", "O", "O", "O", "B-TABLE", "O" ]