question_id int64 0 16.1k | db_id stringclasses 259
values | dber_id stringlengths 15 29 | question stringlengths 16 325 | SQL stringlengths 18 1.25k | tokens listlengths 4 62 | entities listlengths 0 21 | entity_to_token listlengths 20 20 | dber_tags listlengths 4 62 |
|---|---|---|---|---|---|---|---|---|
9,524 | simpson_episodes | bird:train.json:4272 | Please list any three episodes that have an excellent rating. | SELECT title FROM Episode WHERE rating BETWEEN 7 AND 10 LIMIT 3; | [
"Please",
"list",
"any",
"three",
"episodes",
"that",
"have",
"an",
"excellent",
"rating",
"."
] | [
{
"id": 0,
"type": "table",
"value": "episode"
},
{
"id": 2,
"type": "column",
"value": "rating"
},
{
"id": 1,
"type": "column",
"value": "title"
},
{
"id": 4,
"type": "value",
"value": "10"
},
{
"id": 3,
"type": "value",
"value": "7"
}
] | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
9
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
9,525 | movie_2 | bird:test.json:1826 | What movie is playing at both the Odeon and Imperial theater? | SELECT T1.title FROM movies AS T1 JOIN movietheaters AS T2 ON T1.code = T2.movie WHERE T2.name = 'Odeon' INTERSECT SELECT T1.title FROM movies AS T1 JOIN movietheaters AS T2 ON T1.code = T2.movie WHERE T2.name = 'Imperial' | [
"What",
"movie",
"is",
"playing",
"at",
"both",
"the",
"Odeon",
"and",
"Imperial",
"theater",
"?"
] | [
{
"id": 2,
"type": "table",
"value": "movietheaters"
},
{
"id": 5,
"type": "value",
"value": "Imperial"
},
{
"id": 1,
"type": "table",
"value": "movies"
},
{
"id": 0,
"type": "column",
"value": "title"
},
{
"id": 4,
"type": "value",
"value"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
9
... | [
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"B-TABLE",
"O"
] |
9,526 | restaurant_1 | spider:train_spider.json:2825 | Which restaurants have highest rating? List the restaurant name and its rating. | SELECT ResName , Rating FROM Restaurant ORDER BY Rating DESC LIMIT 1; | [
"Which",
"restaurants",
"have",
"highest",
"rating",
"?",
"List",
"the",
"restaurant",
"name",
"and",
"its",
"rating",
"."
] | [
{
"id": 0,
"type": "table",
"value": "restaurant"
},
{
"id": 1,
"type": "column",
"value": "resname"
},
{
"id": 2,
"type": "column",
"value": "rating"
}
] | [
{
"entity_id": 0,
"token_idxs": [
8
]
},
{
"entity_id": 1,
"token_idxs": [
9
]
},
{
"entity_id": 2,
"token_idxs": [
4
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O"
] |
9,527 | social_media | bird:train.json:786 | From which country is the tweet with the most likes posted? | SELECT T2.Country FROM twitter AS T1 INNER JOIN location AS T2 ON T2.LocationID = T1.LocationID ORDER BY T1.Likes DESC LIMIT 1 | [
"From",
"which",
"country",
"is",
"the",
"tweet",
"with",
"the",
"most",
"likes",
"posted",
"?"
] | [
{
"id": 4,
"type": "column",
"value": "locationid"
},
{
"id": 2,
"type": "table",
"value": "location"
},
{
"id": 0,
"type": "column",
"value": "country"
},
{
"id": 1,
"type": "table",
"value": "twitter"
},
{
"id": 3,
"type": "column",
"valu... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
6,
7
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
9
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id":... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"O",
"B-COLUMN",
"O",
"O"
] |
9,528 | student_assessment | spider:train_spider.json:87 | What are the id of students who registered course 301? | SELECT student_id FROM student_course_attendance WHERE course_id = 301 | [
"What",
"are",
"the",
"i",
"d",
"of",
"students",
"who",
"registered",
"course",
"301",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "student_course_attendance"
},
{
"id": 1,
"type": "column",
"value": "student_id"
},
{
"id": 2,
"type": "column",
"value": "course_id"
},
{
"id": 3,
"type": "value",
"value": "301"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": [
9
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"B-VALUE",
"O"
] |
9,529 | advertising_agencies | bird:test.json:2115 | What is the invoice id, status code, and details for the invoice with most number of payments. | SELECT T1.invoice_id , T2.invoice_status , T2.invoice_details FROM Payments AS T1 JOIN Invoices AS T2 ON T1.invoice_id = T2.invoice_id GROUP BY T1.invoice_id ORDER BY count(*) DESC LIMIT 1 | [
"What",
"is",
"the",
"invoice",
"i",
"d",
",",
"status",
"code",
",",
"and",
"details",
"for",
"the",
"invoice",
"with",
"most",
"number",
"of",
"payments",
"."
] | [
{
"id": 2,
"type": "column",
"value": "invoice_details"
},
{
"id": 1,
"type": "column",
"value": "invoice_status"
},
{
"id": 0,
"type": "column",
"value": "invoice_id"
},
{
"id": 3,
"type": "table",
"value": "payments"
},
{
"id": 4,
"type": "ta... | [
{
"entity_id": 0,
"token_idxs": [
3,
4,
5
]
},
{
"entity_id": 1,
"token_idxs": [
6,
7
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
19
]
},
{
"entity_id": 4,
"token_idxs": [
1... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
9,530 | college_3 | spider:train_spider.json:4643 | What is the number of departments in Division "AS"? | SELECT count(*) FROM DEPARTMENT WHERE Division = "AS" | [
"What",
"is",
"the",
"number",
"of",
"departments",
"in",
"Division",
"\"",
"AS",
"\"",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "department"
},
{
"id": 1,
"type": "column",
"value": "division"
},
{
"id": 2,
"type": "column",
"value": "AS"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": [
9
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O"
] |
9,531 | soccer_2 | spider:train_spider.json:4987 | How many students participated in tryouts for each college by descennding count? | SELECT count(*) , cName FROM tryout GROUP BY cName ORDER BY count(*) DESC | [
"How",
"many",
"students",
"participated",
"in",
"tryouts",
"for",
"each",
"college",
"by",
"descennding",
"count",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "tryout"
},
{
"id": 1,
"type": "column",
"value": "cname"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
9,532 | retail_world | bird:train.json:6481 | Compute the total order quantity for Uncle Bob's Organic Dried Pears so far. | SELECT SUM(T2.Quantity) FROM Products AS T1 INNER JOIN `Order Details` AS T2 ON T1.ProductID = T2.ProductID WHERE T1.ProductName LIKE 'Uncle Bob%s Organic Dried Pears' | [
"Compute",
"the",
"total",
"order",
"quantity",
"for",
"Uncle",
"Bob",
"'s",
"Organic",
"Dried",
"Pears",
"so",
"far",
"."
] | [
{
"id": 3,
"type": "value",
"value": "Uncle Bob%s Organic Dried Pears"
},
{
"id": 1,
"type": "table",
"value": "Order Details"
},
{
"id": 2,
"type": "column",
"value": "productname"
},
{
"id": 5,
"type": "column",
"value": "productid"
},
{
"id": 0,... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
6,
7,
8,
9,
10,
11
]
},
{
"entity_id": 4,
"token_idxs": [
... | [
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"O"
] |
9,533 | college_2 | spider:train_spider.json:1417 | What are the names and salaries of instructors who advises students in the History department? | SELECT T2.name , T2.salary FROM advisor AS T1 JOIN instructor AS T2 ON T1.i_id = T2.id JOIN student AS T3 ON T1.s_id = T3.id WHERE T3.dept_name = 'History' | [
"What",
"are",
"the",
"names",
"and",
"salaries",
"of",
"instructors",
"who",
"advises",
"students",
"in",
"the",
"History",
"department",
"?"
] | [
{
"id": 6,
"type": "table",
"value": "instructor"
},
{
"id": 3,
"type": "column",
"value": "dept_name"
},
{
"id": 2,
"type": "table",
"value": "student"
},
{
"id": 4,
"type": "value",
"value": "History"
},
{
"id": 5,
"type": "table",
"value... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": [
14
]
},
{
"entity_id": 4,
"token_idxs": [
13
]
},
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"B-TABLE",
"B-TABLE",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O"
] |
9,534 | world | bird:train.json:7853 | What country declared its independence in 1994? | SELECT Name FROM Country WHERE IndepYear = 1994 | [
"What",
"country",
"declared",
"its",
"independence",
"in",
"1994",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "indepyear"
},
{
"id": 0,
"type": "table",
"value": "country"
},
{
"id": 1,
"type": "column",
"value": "name"
},
{
"id": 3,
"type": "value",
"value": "1994"
}
] | [
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
6
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
9,535 | aan_1 | bird:test.json:993 | List names of all authors who have more than 50 papers. | SELECT T1.name FROM Author AS T1 JOIN Author_list AS T2 ON T1.author_id = T2.author_id GROUP BY T1.author_id HAVING count(*) > 50 | [
"List",
"names",
"of",
"all",
"authors",
"who",
"have",
"more",
"than",
"50",
"papers",
"."
] | [
{
"id": 3,
"type": "table",
"value": "author_list"
},
{
"id": 0,
"type": "column",
"value": "author_id"
},
{
"id": 2,
"type": "table",
"value": "author"
},
{
"id": 1,
"type": "column",
"value": "name"
},
{
"id": 4,
"type": "value",
"value":... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
1
]
},
{
"entity_id": 2,
"token_idxs": [
4
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
9
]
},
{
"entity_id": 5,
"... | [
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O"
] |
9,536 | aan_1 | bird:test.json:971 | How many papers are published in year 2009 by Columbia University? | SELECT count(DISTINCT T1.paper_id) FROM Paper AS T1 JOIN Author_list AS T2 ON T1.paper_id = T2.paper_id JOIN Affiliation AS T3 ON T2.affiliation_id = T3.affiliation_id WHERE T3.name LIKE "Columbia University" AND T1.year = 2009 | [
"How",
"many",
"papers",
"are",
"published",
"in",
"year",
"2009",
"by",
"Columbia",
"University",
"?"
] | [
{
"id": 6,
"type": "column",
"value": "Columbia University"
},
{
"id": 4,
"type": "column",
"value": "affiliation_id"
},
{
"id": 0,
"type": "table",
"value": "affiliation"
},
{
"id": 3,
"type": "table",
"value": "author_list"
},
{
"id": 1,
"typ... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
2
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"B-VALUE",
"O",
"B-COLUMN",
"I-COLUMN",
"O"
] |
9,537 | party_people | spider:train_spider.json:2054 | Which minister left office the latest? | SELECT minister FROM party ORDER BY left_office DESC LIMIT 1 | [
"Which",
"minister",
"left",
"office",
"the",
"latest",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "left_office"
},
{
"id": 1,
"type": "column",
"value": "minister"
},
{
"id": 0,
"type": "table",
"value": "party"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
1
]
},
{
"entity_id": 2,
"token_idxs": [
2,
3
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"toke... | [
"O",
"B-COLUMN",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O"
] |
9,538 | professional_basketball | bird:train.json:2905 | Please list the players who received the "Most Valuable Player" award in the NBA league after the year of 1990, along with their IDs. | SELECT playerID FROM awards_players WHERE year > 1990 AND award = 'Most Valuable Player' AND lgID = 'NBA' | [
"Please",
"list",
"the",
"players",
"who",
"received",
"the",
"\"",
"Most",
"Valuable",
"Player",
"\"",
"award",
"in",
"the",
"NBA",
"league",
"after",
"the",
"year",
"of",
"1990",
",",
"along",
"with",
"their",
"IDs",
"."
] | [
{
"id": 5,
"type": "value",
"value": "Most Valuable Player"
},
{
"id": 0,
"type": "table",
"value": "awards_players"
},
{
"id": 1,
"type": "column",
"value": "playerid"
},
{
"id": 4,
"type": "column",
"value": "award"
},
{
"id": 2,
"type": "col... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
10
]
},
{
"entity_id": 2,
"token_idxs": [
19
]
},
{
"entity_id": 3,
"token_idxs": [
21
]
},
{
"entity_id": 4,
"token_idxs": [
12
]
},... | [
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O"
] |
9,539 | disney | bird:train.json:4626 | The song "Once Upon a Dream" is associated with the movie directed by whom? | SELECT T2.director FROM characters AS T1 INNER JOIN director AS T2 ON T1.movie_title = T2.name WHERE T1.song = 'Once Upon a Dream' | [
"The",
"song",
"\"",
"Once",
"Upon",
"a",
"Dream",
"\"",
"is",
"associated",
"with",
"the",
"movie",
"directed",
"by",
"whom",
"?"
] | [
{
"id": 4,
"type": "value",
"value": "Once Upon a Dream"
},
{
"id": 5,
"type": "column",
"value": "movie_title"
},
{
"id": 1,
"type": "table",
"value": "characters"
},
{
"id": 0,
"type": "column",
"value": "director"
},
{
"id": 2,
"type": "tabl... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
13
]
},
{
"entity_id": 3,
"token_idxs": [
1
]
},
{
"entity_id": 4,
"token_idxs": [
3,
4,
5,
6
]
},
... | [
"O",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-TABLE",
"O",
"O",
"O"
] |
9,540 | loan_1 | spider:train_spider.json:3022 | List the name of all different customers who have some loan sorted by their total loan amount. | SELECT T1.cust_name FROM customer AS T1 JOIN loan AS T2 ON T1.cust_id = T2.cust_id GROUP BY T1.cust_name ORDER BY sum(T2.amount) | [
"List",
"the",
"name",
"of",
"all",
"different",
"customers",
"who",
"have",
"some",
"loan",
"sorted",
"by",
"their",
"total",
"loan",
"amount",
"."
] | [
{
"id": 0,
"type": "column",
"value": "cust_name"
},
{
"id": 1,
"type": "table",
"value": "customer"
},
{
"id": 3,
"type": "column",
"value": "cust_id"
},
{
"id": 4,
"type": "column",
"value": "amount"
},
{
"id": 2,
"type": "table",
"value"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
16
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
9,541 | computer_student | bird:train.json:996 | What is the level of the course with the most number of teachers? | SELECT T1.courseLevel FROM course AS T1 INNER JOIN taughtBy AS T2 ON T1.course_id = T2.course_id GROUP BY T2.course_id ORDER BY COUNT(T2.p_id) DESC LIMIT 1 | [
"What",
"is",
"the",
"level",
"of",
"the",
"course",
"with",
"the",
"most",
"number",
"of",
"teachers",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "courselevel"
},
{
"id": 0,
"type": "column",
"value": "course_id"
},
{
"id": 3,
"type": "table",
"value": "taughtby"
},
{
"id": 2,
"type": "table",
"value": "course"
},
{
"id": 4,
"type": "column",
"va... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
9,542 | driving_school | spider:train_spider.json:6691 | What is the total amount of moeny paid by the customer Carole Bernhard? | SELECT sum(T1.amount_payment) FROM Customer_Payments AS T1 JOIN Customers AS T2 ON T1.customer_id = T2.customer_id WHERE T2.first_name = "Carole" AND T2.last_name = "Bernhard" | [
"What",
"is",
"the",
"total",
"amount",
"of",
"moeny",
"paid",
"by",
"the",
"customer",
"Carole",
"Bernhard",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "customer_payments"
},
{
"id": 2,
"type": "column",
"value": "amount_payment"
},
{
"id": 3,
"type": "column",
"value": "customer_id"
},
{
"id": 4,
"type": "column",
"value": "first_name"
},
{
"id": 1,
"type"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
10
]
},
{
"entity_id": 2,
"token_idxs": [
4,
5,
6
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"B-COLUMN",
"O"
] |
9,544 | works_cycles | bird:train.json:7246 | What is the percentage of employees who work the night shift? | SELECT CAST(SUM(CASE WHEN T1.Name = 'Night' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(T2.BusinessEntityID) FROM Shift AS T1 INNER JOIN EmployeeDepartmentHistory AS T2 ON T1.ShiftId = T2.ShiftId | [
"What",
"is",
"the",
"percentage",
"of",
"employees",
"who",
"work",
"the",
"night",
"shift",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "employeedepartmenthistory"
},
{
"id": 4,
"type": "column",
"value": "businessentityid"
},
{
"id": 2,
"type": "column",
"value": "shiftid"
},
{
"id": 0,
"type": "table",
"value": "shift"
},
{
"id": 8,
"type"... | [
{
"entity_id": 0,
"token_idxs": [
10
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-TABLE",
"O"
] |
9,545 | allergy_1 | spider:train_spider.json:506 | What are the students ids of students who have more than one allergy? | SELECT StuID FROM Has_allergy GROUP BY StuID HAVING count(*) >= 2 | [
"What",
"are",
"the",
"students",
"ids",
"of",
"students",
"who",
"have",
"more",
"than",
"one",
"allergy",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "has_allergy"
},
{
"id": 1,
"type": "column",
"value": "stuid"
},
{
"id": 2,
"type": "value",
"value": "2"
}
] | [
{
"entity_id": 0,
"token_idxs": [
12
]
},
{
"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":... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
9,546 | coinmarketcap | bird:train.json:6250 | What is the total value of Argentum coined traded in the past 24 hours on 2016/10/11. | SELECT T2.volume_24h FROM coins AS T1 INNER JOIN historical AS T2 ON T1.id = T2.coin_id WHERE T1.name = 'Argentum' AND T2.date = '2016-10-11' | [
"What",
"is",
"the",
"total",
"value",
"of",
"Argentum",
"coined",
"traded",
"in",
"the",
"past",
"24",
"hours",
"on",
"2016/10/11",
"."
] | [
{
"id": 0,
"type": "column",
"value": "volume_24h"
},
{
"id": 2,
"type": "table",
"value": "historical"
},
{
"id": 8,
"type": "value",
"value": "2016-10-11"
},
{
"id": 6,
"type": "value",
"value": "Argentum"
},
{
"id": 4,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
9,547 | film_rank | spider:train_spider.json:4123 | Return the maximum and minimum number of cities across all markets. | SELECT max(Number_cities) , min(Number_cities) FROM market | [
"Return",
"the",
"maximum",
"and",
"minimum",
"number",
"of",
"cities",
"across",
"all",
"markets",
"."
] | [
{
"id": 1,
"type": "column",
"value": "number_cities"
},
{
"id": 0,
"type": "table",
"value": "market"
}
] | [
{
"entity_id": 0,
"token_idxs": [
10
]
},
{
"entity_id": 1,
"token_idxs": [
5,
6,
7
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,... | [
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O"
] |
9,548 | university | bird:train.json:8055 | Provide the number of staff at the University of Auckland in 2015. | SELECT CAST(SUM(T1.num_students) AS REAL) / SUM(T1.student_staff_ratio) FROM university_year AS T1 INNER JOIN university AS T2 ON T1.university_id = T2.id WHERE T2.university_name = 'University of Auckland' AND T1.year = 2015 | [
"Provide",
"the",
"number",
"of",
"staff",
"at",
"the",
"University",
"of",
"Auckland",
"in",
"2015",
"."
] | [
{
"id": 5,
"type": "value",
"value": "University of Auckland"
},
{
"id": 8,
"type": "column",
"value": "student_staff_ratio"
},
{
"id": 0,
"type": "table",
"value": "university_year"
},
{
"id": 4,
"type": "column",
"value": "university_name"
},
{
"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
8,
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-VALUE",
"I-VALUE",
"O",
"B-VALUE",
"O"
] |
9,549 | warehouse_1 | bird:test.json:1692 | What are the average and total capacities across all warehouses? | SELECT avg(capacity) , sum(capacity) FROM warehouses | [
"What",
"are",
"the",
"average",
"and",
"total",
"capacities",
"across",
"all",
"warehouses",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "warehouses"
},
{
"id": 1,
"type": "column",
"value": "capacity"
}
] | [
{
"entity_id": 0,
"token_idxs": [
9
]
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O"
] |
9,550 | menu | bird:train.json:5523 | Calculate the total price of items for menu with ID 12882. | SELECT SUM(T2.price) FROM MenuPage AS T1 INNER JOIN MenuItem AS T2 ON T1.id = T2.menu_page_id WHERE T1.menu_id = 12882 | [
"Calculate",
"the",
"total",
"price",
"of",
"items",
"for",
"menu",
"with",
"ID",
"12882",
"."
] | [
{
"id": 6,
"type": "column",
"value": "menu_page_id"
},
{
"id": 0,
"type": "table",
"value": "menupage"
},
{
"id": 1,
"type": "table",
"value": "menuitem"
},
{
"id": 2,
"type": "column",
"value": "menu_id"
},
{
"id": 3,
"type": "value",
"va... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": [
3
]
},
{
"entity... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"B-TABLE",
"B-COLUMN",
"B-VALUE",
"O"
] |
9,551 | customer_complaints | spider:train_spider.json:5777 | What are the emails of customers who have filed complaints on the product which has had the greatest number of complaints? | SELECT t1.email_address FROM customers AS t1 JOIN complaints AS t2 ON t1.customer_id = t2.customer_id GROUP BY t1.customer_id ORDER BY count(*) LIMIT 1 | [
"What",
"are",
"the",
"emails",
"of",
"customers",
"who",
"have",
"filed",
"complaints",
"on",
"the",
"product",
"which",
"has",
"had",
"the",
"greatest",
"number",
"of",
"complaints",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "email_address"
},
{
"id": 0,
"type": "column",
"value": "customer_id"
},
{
"id": 3,
"type": "table",
"value": "complaints"
},
{
"id": 2,
"type": "table",
"value": "customers"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": [
20
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
9,552 | image_and_language | bird:train.json:7505 | State the object class of sample no.10 of image no.2320341. | SELECT T1.OBJ_CLASS FROM OBJ_CLASSES AS T1 INNER JOIN IMG_OBJ AS T2 ON T1.OBJ_CLASS_ID = T2.OBJ_CLASS_ID WHERE T2.IMG_ID = 2320341 AND T2.OBJ_SAMPLE_ID = 10 | [
"State",
"the",
"object",
"class",
"of",
"sample",
"no.10",
"of",
"image",
"no.2320341",
"."
] | [
{
"id": 6,
"type": "column",
"value": "obj_sample_id"
},
{
"id": 3,
"type": "column",
"value": "obj_class_id"
},
{
"id": 1,
"type": "table",
"value": "obj_classes"
},
{
"id": 0,
"type": "column",
"value": "obj_class"
},
{
"id": 2,
"type": "tabl... | [
{
"entity_id": 0,
"token_idxs": [
2,
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
9,553 | cookbook | bird:train.json:8926 | List all the ingredients of Apricot Yogurt Parfaits. | SELECT T3.name, T3.category 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 T1.title = 'Apricot Yogurt Parfaits' | [
"List",
"all",
"the",
"ingredients",
"of",
"Apricot",
"Yogurt",
"Parfaits",
"."
] | [
{
"id": 4,
"type": "value",
"value": "Apricot Yogurt Parfaits"
},
{
"id": 7,
"type": "column",
"value": "ingredient_id"
},
{
"id": 2,
"type": "table",
"value": "ingredient"
},
{
"id": 8,
"type": "column",
"value": "recipe_id"
},
{
"id": 1,
"typ... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
5,
6,
7
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O"
] |
9,554 | movie_platform | bird:train.json:156 | What is Jeannot Szwarc's most popular movie and what is its average rating score? | SELECT T2.movie_title, AVG(T1.rating_score) FROM ratings AS T1 INNER JOIN movies AS T2 ON T1.movie_id = T2.movie_id WHERE T2.director_name = 'Jeannot Szwarc' ORDER BY T2.movie_popularity DESC LIMIT 1 | [
"What",
"is",
"Jeannot",
"Szwarc",
"'s",
"most",
"popular",
"movie",
"and",
"what",
"is",
"its",
"average",
"rating",
"score",
"?"
] | [
{
"id": 5,
"type": "column",
"value": "movie_popularity"
},
{
"id": 4,
"type": "value",
"value": "Jeannot Szwarc"
},
{
"id": 3,
"type": "column",
"value": "director_name"
},
{
"id": 6,
"type": "column",
"value": "rating_score"
},
{
"id": 0,
"ty... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
13
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
2,
3
]
},
{
"entity_id"... | [
"O",
"O",
"B-VALUE",
"I-VALUE",
"O",
"B-COLUMN",
"I-COLUMN",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O"
] |
9,555 | tracking_software_problems | spider:train_spider.json:5357 | For the problem with id 10, return the ids and dates of its problem logs. | SELECT problem_log_id , log_entry_date FROM problem_log WHERE problem_id = 10 | [
"For",
"the",
"problem",
"with",
"i",
"d",
"10",
",",
"return",
"the",
"ids",
"and",
"dates",
"of",
"its",
"problem",
"logs",
"."
] | [
{
"id": 1,
"type": "column",
"value": "problem_log_id"
},
{
"id": 2,
"type": "column",
"value": "log_entry_date"
},
{
"id": 0,
"type": "table",
"value": "problem_log"
},
{
"id": 3,
"type": "column",
"value": "problem_id"
},
{
"id": 4,
"type": "... | [
{
"entity_id": 0,
"token_idxs": [
16
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
15
]
},
{
"entity_id": 4,
"token_idxs": [
6
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-TABLE",
"O"
] |
9,556 | cars | bird:train.json:3085 | Provide the price of the only Toyota Corona hardtop in the database. | SELECT T2.price FROM data AS T1 INNER JOIN price AS T2 ON T1.ID = T2.ID WHERE T1.car_name = 'toyota corona hardtop' | [
"Provide",
"the",
"price",
"of",
"the",
"only",
"Toyota",
"Corona",
"hardtop",
"in",
"the",
"database",
"."
] | [
{
"id": 4,
"type": "value",
"value": "toyota corona hardtop"
},
{
"id": 3,
"type": "column",
"value": "car_name"
},
{
"id": 0,
"type": "column",
"value": "price"
},
{
"id": 2,
"type": "table",
"value": "price"
},
{
"id": 1,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
11
]
},
{
"entity_id": 2,
"token_idxs": [
2
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
6,
7,
8
]
},
{
"e... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"B-TABLE",
"O"
] |
9,557 | cre_Docs_and_Epenses | spider:train_spider.json:6456 | Give the budget type code that is most common among documents with expenses. | SELECT budget_type_code FROM Documents_with_expenses GROUP BY budget_type_code ORDER BY count(*) DESC LIMIT 1 | [
"Give",
"the",
"budget",
"type",
"code",
"that",
"is",
"most",
"common",
"among",
"documents",
"with",
"expenses",
"."
] | [
{
"id": 0,
"type": "table",
"value": "documents_with_expenses"
},
{
"id": 1,
"type": "column",
"value": "budget_type_code"
}
] | [
{
"entity_id": 0,
"token_idxs": [
10,
11,
12
]
},
{
"entity_id": 1,
"token_idxs": [
2,
3,
4
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"I-TABLE",
"O"
] |
9,558 | car_retails | bird:train.json:1552 | How many checks were issued by Euro+ Shopping Channel in the year 2004? | SELECT COUNT(T1.checkNumber) FROM payments AS T1 INNER JOIN customers AS T2 ON T1.customerNumber = T2.customerNumber WHERE customerName = 'Euro+ Shopping Channel' AND STRFTIME('%Y', T1.paymentDate) = '2004' | [
"How",
"many",
"checks",
"were",
"issued",
"by",
"Euro+",
"Shopping",
"Channel",
"in",
"the",
"year",
"2004",
"?"
] | [
{
"id": 5,
"type": "value",
"value": "Euro+ Shopping Channel"
},
{
"id": 3,
"type": "column",
"value": "customernumber"
},
{
"id": 4,
"type": "column",
"value": "customername"
},
{
"id": 2,
"type": "column",
"value": "checknumber"
},
{
"id": 8,
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
2,
3
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
9,559 | candidate_poll | spider:train_spider.json:2431 | What are the names of people who are shorter than average? | SELECT name FROM people WHERE height < (SELECT avg(height) FROM people) | [
"What",
"are",
"the",
"names",
"of",
"people",
"who",
"are",
"shorter",
"than",
"average",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "people"
},
{
"id": 2,
"type": "column",
"value": "height"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O"
] |
9,561 | cre_Drama_Workshop_Groups | spider:train_spider.json:5122 | What are the states or counties of the address of the stores with marketing region code "CA"? | SELECT T1.State_County FROM Addresses AS T1 JOIN Stores AS T2 ON T1.Address_ID = T2.Address_ID WHERE T2.Marketing_Region_Code = "CA" | [
"What",
"are",
"the",
"states",
"or",
"counties",
"of",
"the",
"address",
"of",
"the",
"stores",
"with",
"marketing",
"region",
"code",
"\"",
"CA",
"\"",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "marketing_region_code"
},
{
"id": 0,
"type": "column",
"value": "state_county"
},
{
"id": 5,
"type": "column",
"value": "address_id"
},
{
"id": 1,
"type": "table",
"value": "addresses"
},
{
"id": 2,
"type"... | [
{
"entity_id": 0,
"token_idxs": [
3,
4,
5
]
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": [
13,
14,
15
]
},
{
"entity_id": 4,
... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"O",
"O"
] |
9,563 | movies_4 | bird:train.json:495 | What is the genre of the movie title which was well-received by the audiences but made the lowest revenue? | SELECT T3.genre_name FROM movie AS T1 INNER JOIN movie_genres AS T2 ON T1.movie_id = T2.movie_id INNER JOIN genre AS T3 ON T2.genre_id = T3.genre_id ORDER BY T1.vote_average DESC, T1.revenue LIMIT 1 | [
"What",
"is",
"the",
"genre",
"of",
"the",
"movie",
"title",
"which",
"was",
"well",
"-",
"received",
"by",
"the",
"audiences",
"but",
"made",
"the",
"lowest",
"revenue",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "vote_average"
},
{
"id": 5,
"type": "table",
"value": "movie_genres"
},
{
"id": 0,
"type": "column",
"value": "genre_name"
},
{
"id": 6,
"type": "column",
"value": "genre_id"
},
{
"id": 7,
"type": "column"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
20
]
},
{
"entity_id": 4,
"token_idxs": [
6
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
9,564 | movielens | bird:train.json:2321 | List the user ids and ages who gave the rate 2 to the movie No. 2409051. | SELECT T1.userid, T1.age FROM users AS T1 INNER JOIN u2base AS T2 ON T1.userid = T2.userid WHERE T2.movieid = '2409051' AND T2.rating = 2 | [
"List",
"the",
"user",
"ids",
"and",
"ages",
"who",
"gave",
"the",
"rate",
"2",
"to",
"the",
"movie",
"No",
".",
"2409051",
"."
] | [
{
"id": 4,
"type": "column",
"value": "movieid"
},
{
"id": 5,
"type": "value",
"value": "2409051"
},
{
"id": 0,
"type": "column",
"value": "userid"
},
{
"id": 3,
"type": "table",
"value": "u2base"
},
{
"id": 6,
"type": "column",
"value": "r... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
2
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
13
]
},
{
"entity... | [
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"B-VALUE",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O"
] |
9,565 | cs_semester | bird:train.json:912 | Describe the students' full names and GPAs under the supervision of the most popular professor. | SELECT T3.f_name, T3.l_name, T3.gpa FROM prof AS T1 INNER JOIN RA AS T2 ON T1.prof_id = T2.prof_id INNER JOIN student AS T3 ON T2.student_id = T3.student_id ORDER BY T1.popularity DESC LIMIT 1 | [
"Describe",
"the",
"students",
"'",
"full",
"names",
"and",
"GPAs",
"under",
"the",
"supervision",
"of",
"the",
"most",
"popular",
"professor",
"."
] | [
{
"id": 4,
"type": "column",
"value": "popularity"
},
{
"id": 7,
"type": "column",
"value": "student_id"
},
{
"id": 3,
"type": "table",
"value": "student"
},
{
"id": 8,
"type": "column",
"value": "prof_id"
},
{
"id": 0,
"type": "column",
"v... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": [
2
]
},
{
"entity_id": 4,
"token_idxs": [
14
]
},
{
"entity... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"O"
] |
9,566 | ship_mission | spider:train_spider.json:4001 | what are the names of the ships ordered by ascending tonnage? | SELECT Name FROM ship ORDER BY Tonnage ASC | [
"what",
"are",
"the",
"names",
"of",
"the",
"ships",
"ordered",
"by",
"ascending",
"tonnage",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "tonnage"
},
{
"id": 0,
"type": "table",
"value": "ship"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
10
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
9,567 | food_inspection | bird:train.json:8777 | Among the restaurants being inspected in 2016, how many of them are in San Francisco? | SELECT COUNT(DISTINCT T2.business_id) FROM inspections AS T1 INNER JOIN businesses AS T2 ON T1.business_id = T2.business_id WHERE STRFTIME('%Y', T1.`date`) = '2016' AND T2.city IN ('San Francisco', 'SAN FRANCISCO', 'SF', 'S.F.') | [
"Among",
"the",
"restaurants",
"being",
"inspected",
"in",
"2016",
",",
"how",
"many",
"of",
"them",
"are",
"in",
"San",
"Francisco",
"?"
] | [
{
"id": 5,
"type": "value",
"value": "San Francisco"
},
{
"id": 6,
"type": "value",
"value": "SAN FRANCISCO"
},
{
"id": 0,
"type": "table",
"value": "inspections"
},
{
"id": 2,
"type": "column",
"value": "business_id"
},
{
"id": 1,
"type": "tab... | [
{
"entity_id": 0,
"token_idxs": [
4,
5
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
6
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"toke... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O"
] |
9,568 | movie_platform | bird:train.json:151 | Between 1970 to 1980, how many movies with a popularity of more than 11,000 were released? | SELECT COUNT(movie_id) FROM movies WHERE movie_release_year BETWEEN '1970' AND '1980' AND movie_popularity > 11000 | [
"Between",
"1970",
"to",
"1980",
",",
"how",
"many",
"movies",
"with",
"a",
"popularity",
"of",
"more",
"than",
"11,000",
"were",
"released",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "movie_release_year"
},
{
"id": 5,
"type": "column",
"value": "movie_popularity"
},
{
"id": 1,
"type": "column",
"value": "movie_id"
},
{
"id": 0,
"type": "table",
"value": "movies"
},
{
"id": 6,
"type": "v... | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
1
]
},
{
"entity_id": 4,
"token_idxs": [
3
]
},
{
"entity_id": 5,
"... | [
"O",
"B-VALUE",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O"
] |
9,570 | e_government | spider:train_spider.json:6331 | What are the names of all cities and states? | SELECT town_city FROM addresses UNION SELECT state_province_county FROM addresses | [
"What",
"are",
"the",
"names",
"of",
"all",
"cities",
"and",
"states",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "state_province_county"
},
{
"id": 0,
"type": "table",
"value": "addresses"
},
{
"id": 1,
"type": "column",
"value": "town_city"
}
] | [
{
"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... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
9,572 | synthea | bird:train.json:1364 | For how long was Elly Koss required to take Acetaminophen? | SELECT strftime('%J', T2.STOP) - strftime('%J', T2.START) AS days FROM patients AS T1 INNER JOIN medications AS T2 ON T1.patient = T2.PATIENT WHERE T1.first = 'Elly' AND T1.last = 'Koss' AND T2.description LIKE 'Acetaminophen%' | [
"For",
"how",
"long",
"was",
"Elly",
"Koss",
"required",
"to",
"take",
"Acetaminophen",
"?"
] | [
{
"id": 8,
"type": "value",
"value": "Acetaminophen%"
},
{
"id": 1,
"type": "table",
"value": "medications"
},
{
"id": 7,
"type": "column",
"value": "description"
},
{
"id": 0,
"type": "table",
"value": "patients"
},
{
"id": 2,
"type": "column"... | [
{
"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": [
4
]
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"B-VALUE",
"B-VALUE",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
9,573 | restaurant | bird:train.json:1738 | What percentage of restaurants are from the Bay Area? | SELECT CAST(SUM(IIF(T1.region = 'bay area', 1, 0)) AS REAL) * 100 / COUNT(T2.id_restaurant) FROM geographic AS T1 INNER JOIN location AS T2 ON T1.city = T2.city | [
"What",
"percentage",
"of",
"restaurants",
"are",
"from",
"the",
"Bay",
"Area",
"?"
] | [
{
"id": 4,
"type": "column",
"value": "id_restaurant"
},
{
"id": 0,
"type": "table",
"value": "geographic"
},
{
"id": 1,
"type": "table",
"value": "location"
},
{
"id": 8,
"type": "value",
"value": "bay area"
},
{
"id": 7,
"type": "column",
... | [
{
"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": [
3
]
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O"
] |
9,574 | shakespeare | bird:train.json:3001 | How many characters are there in Hamlet? | SELECT COUNT(DISTINCT T3.character_id) FROM works AS T1 INNER JOIN chapters AS T2 ON T1.id = T2.work_id INNER JOIN paragraphs AS T3 ON T2.id = T3.chapter_id WHERE T1.Title = 'Hamlet' | [
"How",
"many",
"characters",
"are",
"there",
"in",
"Hamlet",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "character_id"
},
{
"id": 0,
"type": "table",
"value": "paragraphs"
},
{
"id": 7,
"type": "column",
"value": "chapter_id"
},
{
"id": 5,
"type": "table",
"value": "chapters"
},
{
"id": 8,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
2
... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
9,575 | debate | spider:train_spider.json:1493 | List the venues of debates in ascending order of the number of audience. | SELECT Venue FROM debate ORDER BY Num_of_Audience ASC | [
"List",
"the",
"venues",
"of",
"debates",
"in",
"ascending",
"order",
"of",
"the",
"number",
"of",
"audience",
"."
] | [
{
"id": 2,
"type": "column",
"value": "num_of_audience"
},
{
"id": 0,
"type": "table",
"value": "debate"
},
{
"id": 1,
"type": "column",
"value": "venue"
}
] | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
10,
11,
12
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
... | [
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O"
] |
9,576 | movie_3 | bird:train.json:9309 | How many films did a customer named Francis Sikes rent? | SELECT COUNT(T1.customer_id) FROM customer AS T1 INNER JOIN rental AS T2 ON T1.customer_id = T2.customer_id WHERE T1.first_name = 'FRANCIS' AND T1.last_name = 'SIKES' | [
"How",
"many",
"films",
"did",
"a",
"customer",
"named",
"Francis",
"Sikes",
"rent",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "customer_id"
},
{
"id": 3,
"type": "column",
"value": "first_name"
},
{
"id": 5,
"type": "column",
"value": "last_name"
},
{
"id": 0,
"type": "table",
"value": "customer"
},
{
"id": 4,
"type": "value",
... | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
9
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
7
]
},
{
"entity_id": 5,
"... | [
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"B-VALUE",
"B-TABLE",
"O"
] |
9,578 | ice_hockey_draft | bird:train.json:6961 | Which team does Andreas Jamtin belong to? | SELECT DISTINCT T1.TEAM FROM SeasonStatus AS T1 INNER JOIN PlayerInfo AS T2 ON T1.ELITEID = T2.ELITEID WHERE T2.PlayerName = 'Andreas Jamtin' | [
"Which",
"team",
"does",
"Andreas",
"Jamtin",
"belong",
"to",
"?"
] | [
{
"id": 4,
"type": "value",
"value": "Andreas Jamtin"
},
{
"id": 1,
"type": "table",
"value": "seasonstatus"
},
{
"id": 2,
"type": "table",
"value": "playerinfo"
},
{
"id": 3,
"type": "column",
"value": "playername"
},
{
"id": 5,
"type": "colum... | [
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
3,
4
]
},
{
"entity_id": 5,
"toke... | [
"O",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"O",
"O",
"O"
] |
9,579 | pilot_1 | bird:test.json:1163 | For each city, find the number and average age of pilots who have a plane. | SELECT count(T1.pilot_name) , avg(T1.age) , T2.location FROM pilotskills AS T1 JOIN hangar AS T2 ON T1.plane_name = T2.plane_name GROUP BY T2.location | [
"For",
"each",
"city",
",",
"find",
"the",
"number",
"and",
"average",
"age",
"of",
"pilots",
"who",
"have",
"a",
"plane",
"."
] | [
{
"id": 1,
"type": "table",
"value": "pilotskills"
},
{
"id": 3,
"type": "column",
"value": "pilot_name"
},
{
"id": 5,
"type": "column",
"value": "plane_name"
},
{
"id": 0,
"type": "column",
"value": "location"
},
{
"id": 2,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
11
]
},
{
"entity_id": 4,
"token_idxs": [
9
]
},
{
"entity_id": 5,
"token_idxs":... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
9,580 | institution_sports | bird:test.json:1670 | Return the most common type of affiliation across all institutions. | SELECT Affiliation FROM institution GROUP BY Affiliation ORDER BY COUNT(*) DESC LIMIT 1 | [
"Return",
"the",
"most",
"common",
"type",
"of",
"affiliation",
"across",
"all",
"institutions",
"."
] | [
{
"id": 0,
"type": "table",
"value": "institution"
},
{
"id": 1,
"type": "column",
"value": "affiliation"
}
] | [
{
"entity_id": 0,
"token_idxs": [
9
]
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O"
] |
9,581 | professional_basketball | bird:train.json:2883 | List the full name of players who are born outside USA. | SELECT firstName, middleName, lastName FROM players WHERE birthCountry != 'USA' | [
"List",
"the",
"full",
"name",
"of",
"players",
"who",
"are",
"born",
"outside",
"USA",
"."
] | [
{
"id": 4,
"type": "column",
"value": "birthcountry"
},
{
"id": 2,
"type": "column",
"value": "middlename"
},
{
"id": 1,
"type": "column",
"value": "firstname"
},
{
"id": 3,
"type": "column",
"value": "lastname"
},
{
"id": 0,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
3
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
9,582 | college_completion | bird:train.json:3720 | Tell the number of 4-year public schools in UT whose graduation rate exceeds the average for the state. | SELECT COUNT(DISTINCT T1.chronname) FROM institution_details AS T1 INNER JOIN state_sector_grads AS T2 ON T2.state = T1.state WHERE T2.state_abbr = 'UT' AND T1.level = '4-year' AND T1.control = 'Public' AND T1.awards_per_value > T1.awards_per_state_value | [
"Tell",
"the",
"number",
"of",
"4",
"-",
"year",
"public",
"schools",
"in",
"UT",
"whose",
"graduation",
"rate",
"exceeds",
"the",
"average",
"for",
"the",
"state",
"."
] | [
{
"id": 11,
"type": "column",
"value": "awards_per_state_value"
},
{
"id": 0,
"type": "table",
"value": "institution_details"
},
{
"id": 1,
"type": "table",
"value": "state_sector_grads"
},
{
"id": 10,
"type": "column",
"value": "awards_per_value"
},
{... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
19
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
10
... | [
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"B-VALUE",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
9,583 | works_cycles | bird:train.json:7127 | Which chromoly steel product model has AdventureWorks saved in English? | SELECT T1.ProductModelID FROM ProductModelProductDescriptionCulture AS T1 INNER JOIN Culture AS T2 USING (cultureid) INNER JOIN ProductDescription AS T3 USING (productdescriptionid) WHERE T3.Description LIKE 'Chromoly steel%' AND T2.Name = 'English' | [
"Which",
"chromoly",
"steel",
"product",
"model",
"has",
"AdventureWorks",
"saved",
"in",
"English",
"?"
] | [
{
"id": 2,
"type": "table",
"value": "productmodelproductdescriptionculture"
},
{
"id": 1,
"type": "table",
"value": "productdescription"
},
{
"id": 5,
"type": "value",
"value": "Chromoly steel%"
},
{
"id": 0,
"type": "column",
"value": "productmodelid"
... | [
{
"entity_id": 0,
"token_idxs": [
3,
4
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": [
... | [
"O",
"B-VALUE",
"I-VALUE",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
9,584 | financial | bird:dev.json:105 | There was a loan approved in 1994/8/25, where was that account opened, give the district Id of the branch. | SELECT T1.district_id FROM account AS T1 INNER JOIN loan AS T2 ON T1.account_id = T2.account_id WHERE T2.date = '1994-08-25' | [
"There",
"was",
"a",
"loan",
"approved",
"in",
"1994/8/25",
",",
"where",
"was",
"that",
"account",
"opened",
",",
"give",
"the",
"district",
"I",
"d",
"of",
"the",
"branch",
"."
] | [
{
"id": 0,
"type": "column",
"value": "district_id"
},
{
"id": 4,
"type": "value",
"value": "1994-08-25"
},
{
"id": 5,
"type": "column",
"value": "account_id"
},
{
"id": 1,
"type": "table",
"value": "account"
},
{
"id": 2,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": [
16,
17,
18
]
},
{
"entity_id": 1,
"token_idxs": [
11
]
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
6
]... | [
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O"
] |
9,585 | superhero | bird:dev.json:807 | What is the superpowers of the superhero has Helen Parr as their full name? | SELECT T3.power_name FROM superhero AS T1 INNER JOIN hero_power AS T2 ON T1.id = T2.hero_id INNER JOIN superpower AS T3 ON T2.power_id = T3.id WHERE T1.full_name = 'Helen Parr' | [
"What",
"is",
"the",
"superpowers",
"of",
"the",
"superhero",
"has",
"Helen",
"Parr",
"as",
"their",
"full",
"name",
"?"
] | [
{
"id": 0,
"type": "column",
"value": "power_name"
},
{
"id": 1,
"type": "table",
"value": "superpower"
},
{
"id": 3,
"type": "value",
"value": "Helen Parr"
},
{
"id": 5,
"type": "table",
"value": "hero_power"
},
{
"id": 2,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
12,
13
]
},
{
"entity_id": 3,
"token_idxs": [
8,
9
]
},
{
"entity_id": 4,
"token_idxs": [
6
]
},
{
"... | [
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"I-VALUE",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O"
] |
9,586 | cs_semester | bird:train.json:888 | How many courses have the highest difficulty? | SELECT COUNT(course_id) FROM course WHERE diff = 5 | [
"How",
"many",
"courses",
"have",
"the",
"highest",
"difficulty",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "course_id"
},
{
"id": 0,
"type": "table",
"value": "course"
},
{
"id": 1,
"type": "column",
"value": "diff"
},
{
"id": 2,
"type": "value",
"value": "5"
}
] | [
{
"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": []
},
{
... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O"
] |
9,587 | menu | bird:train.json:5471 | Which dish lasted longer, Anchovies or Fresh lobsters in every style? | SELECT CASE WHEN SUM(CASE WHEN name = 'Anchovies' THEN last_appeared - first_appeared ELSE 0 END) - SUM(CASE WHEN name = 'Fresh lobsters in every style' THEN last_appeared - first_appeared ELSE 0 END) > 0 THEN 'Anchovies' ELSE 'Fresh lobsters in every style' END FROM Dish WHERE name IN ('Fresh lobsters in every style',... | [
"Which",
"dish",
"lasted",
"longer",
",",
"Anchovies",
"or",
"Fresh",
"lobsters",
"in",
"every",
"style",
"?"
] | [
{
"id": 2,
"type": "value",
"value": "Fresh lobsters in every style"
},
{
"id": 6,
"type": "column",
"value": "first_appeared"
},
{
"id": 5,
"type": "column",
"value": "last_appeared"
},
{
"id": 3,
"type": "value",
"value": "Anchovies"
},
{
"id": 0... | [
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
7,
8,
9,
10,
11
]
},
{
"entity_id": 3,
"token_idxs": [
5
]
},
{
"entity_id": 4,
"token_idxs":... | [
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O"
] |
9,588 | donor | bird:train.json:3201 | Please list the donation messages of the donations for the projects created by a teacher working in a public magnet school in Brooklyn. | SELECT T2.donation_message FROM projects AS T1 INNER JOIN donations AS T2 ON T1.projectid = T2.projectid WHERE T1.school_city = 'Brooklyn' AND T1.school_magnet = 't' | [
"Please",
"list",
"the",
"donation",
"messages",
"of",
"the",
"donations",
"for",
"the",
"projects",
"created",
"by",
"a",
"teacher",
"working",
"in",
"a",
"public",
"magnet",
"school",
"in",
"Brooklyn",
"."
] | [
{
"id": 0,
"type": "column",
"value": "donation_message"
},
{
"id": 6,
"type": "column",
"value": "school_magnet"
},
{
"id": 4,
"type": "column",
"value": "school_city"
},
{
"id": 2,
"type": "table",
"value": "donations"
},
{
"id": 3,
"type": "... | [
{
"entity_id": 0,
"token_idxs": [
3,
4
]
},
{
"entity_id": 1,
"token_idxs": [
10
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
20
]
},
{
... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"B-VALUE",
"O"
] |
9,589 | device | spider:train_spider.json:5080 | List the software platform shared by the greatest number of devices. | SELECT Software_Platform FROM device GROUP BY Software_Platform ORDER BY COUNT(*) DESC LIMIT 1 | [
"List",
"the",
"software",
"platform",
"shared",
"by",
"the",
"greatest",
"number",
"of",
"devices",
"."
] | [
{
"id": 1,
"type": "column",
"value": "software_platform"
},
{
"id": 0,
"type": "table",
"value": "device"
}
] | [
{
"entity_id": 0,
"token_idxs": [
10
]
},
{
"entity_id": 1,
"token_idxs": [
2,
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"tok... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
9,590 | european_football_2 | bird:dev.json:1040 | List the top 10 players' names whose heights are above 180 in descending order of average heading accuracy. | SELECT t1.player_name FROM Player AS t1 INNER JOIN Player_Attributes AS t2 ON t1.player_api_id = t2.player_api_id WHERE t1.height > 180 GROUP BY t1.id ORDER BY CAST(SUM(t2.heading_accuracy) AS REAL) / COUNT(t2.`player_fifa_api_id`) DESC LIMIT 10 | [
"List",
"the",
"top",
"10",
"players",
"'",
"names",
"whose",
"heights",
"are",
"above",
"180",
"in",
"descending",
"order",
"of",
"average",
"heading",
"accuracy",
"."
] | [
{
"id": 7,
"type": "column",
"value": "player_fifa_api_id"
},
{
"id": 3,
"type": "table",
"value": "player_attributes"
},
{
"id": 8,
"type": "column",
"value": "heading_accuracy"
},
{
"id": 6,
"type": "column",
"value": "player_api_id"
},
{
"id": 1... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
5,
6
]
},
{
"entity_id": 2,
"token_idxs": [
4
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
8
]
},
{
"entity_id":... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O"
] |
9,591 | legislator | bird:train.json:4823 | Give the religion of the legislator whose YouTube name is MaxineWaters. | SELECT T2.religion_bio FROM `social-media` AS T1 INNER JOIN current AS T2 ON T1.bioguide = T2.bioguide_id WHERE T1.youtube = 'MaxineWaters' | [
"Give",
"the",
"religion",
"of",
"the",
"legislator",
"whose",
"YouTube",
"name",
"is",
"MaxineWaters",
"."
] | [
{
"id": 0,
"type": "column",
"value": "religion_bio"
},
{
"id": 1,
"type": "table",
"value": "social-media"
},
{
"id": 4,
"type": "value",
"value": "MaxineWaters"
},
{
"id": 6,
"type": "column",
"value": "bioguide_id"
},
{
"id": 5,
"type": "col... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
7
]
},
{
"entity_id": 4,
"token_idxs": [
10
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O"
] |
9,592 | disney | bird:train.json:4704 | How many movies were released between 1937 and 1950? | SELECT COUNT(movie_title) FROM characters WHERE SUBSTR(release_date, LENGTH(release_date) - 1, LENGTH(release_date)) BETWEEN '37' AND '50' | [
"How",
"many",
"movies",
"were",
"released",
"between",
"1937",
"and",
"1950",
"?"
] | [
{
"id": 4,
"type": "column",
"value": "release_date"
},
{
"id": 3,
"type": "column",
"value": "movie_title"
},
{
"id": 0,
"type": "table",
"value": "characters"
},
{
"id": 1,
"type": "value",
"value": "37"
},
{
"id": 2,
"type": "value",
"va... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": [
2
]
},
{
"entity_id": 4,
"token_idxs": [
4
]
},
{
"entity_... | [
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"B-VALUE",
"O"
] |
9,593 | epinions_1 | spider:train_spider.json:1691 | Find the number of reviews. | SELECT count(*) FROM review | [
"Find",
"the",
"number",
"of",
"reviews",
"."
] | [
{
"id": 0,
"type": "table",
"value": "review"
}
] | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
9,594 | retail_complains | bird:train.json:343 | Among the teenager clients who use Google account and Microsoft account, which group of client is more than the other? | SELECT CASE WHEN SUM(CASE WHEN email LIKE '%@gmail.com' THEN 1 ELSE 0 END) > SUM(CASE WHEN email LIKE '%@outlook.com' THEN 1 ELSE 0 END) THEN 'Google account' ELSE 'Microsoft account' END FROM client WHERE age BETWEEN 13 AND 19 | [
"Among",
"the",
"teenager",
"clients",
"who",
"use",
"Google",
"account",
"and",
"Microsoft",
"account",
",",
"which",
"group",
"of",
"client",
"is",
"more",
"than",
"the",
"other",
"?"
] | [
{
"id": 4,
"type": "value",
"value": "Microsoft account"
},
{
"id": 5,
"type": "value",
"value": "Google account"
},
{
"id": 10,
"type": "value",
"value": "%@outlook.com"
},
{
"id": 9,
"type": "value",
"value": "%@gmail.com"
},
{
"id": 0,
"type... | [
{
"entity_id": 0,
"token_idxs": [
15
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
9,
10
]
},
{
"entity_id": 5,
"to... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O",
"B-VALUE",
"I-VALUE",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O"
] |
9,595 | gymnast | spider:train_spider.json:1754 | What is the total point count of the youngest gymnast? | SELECT T1.Total_Points FROM gymnast AS T1 JOIN people AS T2 ON T1.Gymnast_ID = T2.People_ID ORDER BY T2.Age ASC LIMIT 1 | [
"What",
"is",
"the",
"total",
"point",
"count",
"of",
"the",
"youngest",
"gymnast",
"?"
] | [
{
"id": 0,
"type": "column",
"value": "total_points"
},
{
"id": 4,
"type": "column",
"value": "gymnast_id"
},
{
"id": 5,
"type": "column",
"value": "people_id"
},
{
"id": 1,
"type": "table",
"value": "gymnast"
},
{
"id": 2,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": [
3,
4
]
},
{
"entity_id": 1,
"token_idxs": [
9
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"toke... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
9,596 | public_review_platform | bird:train.json:4136 | What is the opening time of the active businesses in Glendale that have a medium review count. | SELECT DISTINCT T2.opening_time FROM Business AS T1 INNER JOIN Business_Hours AS T2 ON T1.business_id = T2.business_id INNER JOIN Days AS T3 ON T2.day_id = T3.day_id WHERE T1.city = 'Glendale' AND T1.review_count = 'Medium' AND T1.active = 'true' | [
"What",
"is",
"the",
"opening",
"time",
"of",
"the",
"active",
"businesses",
"in",
"Glendale",
"that",
"have",
"a",
"medium",
"review",
"count",
"."
] | [
{
"id": 3,
"type": "table",
"value": "business_hours"
},
{
"id": 0,
"type": "column",
"value": "opening_time"
},
{
"id": 7,
"type": "column",
"value": "review_count"
},
{
"id": 11,
"type": "column",
"value": "business_id"
},
{
"id": 2,
"type": ... | [
{
"entity_id": 0,
"token_idxs": [
3,
4
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"toke... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-COLUMN",
"B-TABLE",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"I-COLUMN",
"O"
] |
9,597 | donor | bird:train.json:3237 | What is the total donation amount for the project 'Engaging Young Readers with a Leveled Classroom Library'? | SELECT SUM(T2.donation_to_project) + SUM(T2.donation_optional_support) FROM essays AS T1 INNER JOIN donations AS T2 ON T1.projectid = T2.projectid WHERE T1.title LIKE 'Engaging Young Readers with a Leveled Classroom Library ' | [
"What",
"is",
"the",
"total",
"donation",
"amount",
"for",
"the",
"project",
"'",
"Engaging",
"Young",
"Readers",
"with",
"a",
"Leveled",
"Classroom",
"Library",
"'",
"?"
] | [
{
"id": 3,
"type": "value",
"value": "Engaging Young Readers with a Leveled Classroom Library "
},
{
"id": 6,
"type": "column",
"value": "donation_optional_support"
},
{
"id": 5,
"type": "column",
"value": "donation_to_project"
},
{
"id": 1,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": [
10,
11,
12,
13,
14,
15,
16,
17
]
},
{
... | [
"O",
"O",
"O",
"B-COLUMN",
"B-TABLE",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O"
] |
9,598 | donor | bird:train.json:3188 | State the name of vendor that supplies book resources to all school with literacy subject as their primary focus. | SELECT DISTINCT T1.vendor_name FROM resources AS T1 INNER JOIN projects AS T2 ON T1.projectid = T2.projectid WHERE T2.primary_focus_subject LIKE 'Literacy' | [
"State",
"the",
"name",
"of",
"vendor",
"that",
"supplies",
"book",
"resources",
"to",
"all",
"school",
"with",
"literacy",
"subject",
"as",
"their",
"primary",
"focus",
"."
] | [
{
"id": 3,
"type": "column",
"value": "primary_focus_subject"
},
{
"id": 0,
"type": "column",
"value": "vendor_name"
},
{
"id": 1,
"type": "table",
"value": "resources"
},
{
"id": 5,
"type": "column",
"value": "projectid"
},
{
"id": 2,
"type": ... | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
17,
18
]
},
{
"entity_id": 4,
"token_idxs": [
13
]
},
{
... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O"
] |
9,599 | student_loan | bird:train.json:4522 | State the number of disabled students who have payment due. | SELECT COUNT(T1.name) FROM no_payment_due AS T1 INNER JOIN disabled AS T2 ON T1.name = T2.name WHERE T1.bool = 'neg' | [
"State",
"the",
"number",
"of",
"disabled",
"students",
"who",
"have",
"payment",
"due",
"."
] | [
{
"id": 0,
"type": "table",
"value": "no_payment_due"
},
{
"id": 1,
"type": "table",
"value": "disabled"
},
{
"id": 2,
"type": "column",
"value": "bool"
},
{
"id": 4,
"type": "column",
"value": "name"
},
{
"id": 3,
"type": "value",
"value":... | [
{
"entity_id": 0,
"token_idxs": [
8,
9
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
2
]
},
{
"entity_id":... | [
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"O"
] |
9,600 | world_development_indicators | bird:train.json:2223 | What is the series code for number of infant deaths in year 1965 for the country whose full name is Islamic State of Afghanistan? | SELECT DISTINCT T3.Seriescode FROM Country AS T1 INNER JOIN Indicators AS T2 ON T1.CountryCode = T2.CountryCode INNER JOIN CountryNotes AS T3 ON T2.CountryCode = T3.Countrycode WHERE T2.IndicatorName = 'Number of infant deaths' AND T1.LongName = 'Islamic State of Afghanistan' AND T2.Year = 1965 | [
"What",
"is",
"the",
"series",
"code",
"for",
"number",
"of",
"infant",
"deaths",
"in",
"year",
"1965",
"for",
"the",
"country",
"whose",
"full",
"name",
"is",
"Islamic",
"State",
"of",
"Afghanistan",
"?"
] | [
{
"id": 8,
"type": "value",
"value": "Islamic State of Afghanistan"
},
{
"id": 6,
"type": "value",
"value": "Number of infant deaths"
},
{
"id": 5,
"type": "column",
"value": "indicatorname"
},
{
"id": 1,
"type": "table",
"value": "countrynotes"
},
{
... | [
{
"entity_id": 0,
"token_idxs": [
3,
4
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
15
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"tok... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"B-COLUMN",
"B-VALUE",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O"
] |
9,601 | college_2 | spider:train_spider.json:1426 | Find the names of students who have taken any course in the fall semester of year 2003. | SELECT name FROM student WHERE id IN (SELECT id FROM takes WHERE semester = 'Fall' AND YEAR = 2003) | [
"Find",
"the",
"names",
"of",
"students",
"who",
"have",
"taken",
"any",
"course",
"in",
"the",
"fall",
"semester",
"of",
"year",
"2003",
"."
] | [
{
"id": 4,
"type": "column",
"value": "semester"
},
{
"id": 0,
"type": "table",
"value": "student"
},
{
"id": 3,
"type": "table",
"value": "takes"
},
{
"id": 1,
"type": "column",
"value": "name"
},
{
"id": 5,
"type": "value",
"value": "Fall... | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
0
]
},
{
"entity_id": 3,
"token_idxs": [
7
]
},
{
"entity_id": 4,
"token_idxs": [
13
]
},
... | [
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O",
"B-COLUMN",
"B-VALUE",
"O"
] |
9,602 | manufactory_1 | spider:train_spider.json:5340 | Select the average price of each manufacturer's products, showing only the manufacturer's code. | SELECT AVG(Price) , Manufacturer FROM Products GROUP BY Manufacturer | [
"Select",
"the",
"average",
"price",
"of",
"each",
"manufacturer",
"'s",
"products",
",",
"showing",
"only",
"the",
"manufacturer",
"'s",
"code",
"."
] | [
{
"id": 1,
"type": "column",
"value": "manufacturer"
},
{
"id": 0,
"type": "table",
"value": "products"
},
{
"id": 2,
"type": "column",
"value": "price"
}
] | [
{
"entity_id": 0,
"token_idxs": [
8
]
},
{
"entity_id": 1,
"token_idxs": [
13
]
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O"
] |
9,603 | world_development_indicators | bird:train.json:2121 | Find the countries in south Asia which are in the low-income group. What is the source of their recent income and expenditure data? List it alongside the table name of the countries. | SELECT TableName, SourceOfMostRecentIncomeAndExpenditureData FROM Country WHERE Region = 'South Asia' AND IncomeGroup = 'Low income' | [
"Find",
"the",
"countries",
"in",
"south",
"Asia",
"which",
"are",
"in",
"the",
"low",
"-",
"income",
"group",
".",
"What",
"is",
"the",
"source",
"of",
"their",
"recent",
"income",
"and",
"expenditure",
"data",
"?",
"List",
"it",
"alongside",
"the",
"ta... | [
{
"id": 2,
"type": "column",
"value": "sourceofmostrecentincomeandexpendituredata"
},
{
"id": 5,
"type": "column",
"value": "incomegroup"
},
{
"id": 4,
"type": "value",
"value": "South Asia"
},
{
"id": 6,
"type": "value",
"value": "Low income"
},
{
... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
31,
32
]
},
{
"entity_id": 2,
"token_idxs": [
21,
22,
23,
24,
25
]
},
{
"entity_id": 3,
"token_idxs": [
7,
8
]
},
{
... | [
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"I-VALUE",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-COLU... |
9,604 | retails | bird:train.json:6858 | What is the comment of the product "burlywood plum powder puff mint"? | SELECT p_comment FROM part WHERE p_name = 'burlywood plum powder puff mint' | [
"What",
"is",
"the",
"comment",
"of",
"the",
"product",
"\"",
"burlywood",
"plum",
"powder",
"puff",
"mint",
"\"",
"?"
] | [
{
"id": 3,
"type": "value",
"value": "burlywood plum powder puff mint"
},
{
"id": 1,
"type": "column",
"value": "p_comment"
},
{
"id": 2,
"type": "column",
"value": "p_name"
},
{
"id": 0,
"type": "table",
"value": "part"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
8,
9,
10,
11,
12
]
},
{
"entity_id": 4,
"token_idxs": []
},
{... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O"
] |
9,605 | computer_student | bird:train.json:992 | Please list the IDs of the advisors of the students who are in the 5th year of their program. | SELECT T1.p_id_dummy FROM advisedBy AS T1 INNER JOIN person AS T2 ON T1.p_id = T2.p_id WHERE T2.yearsInProgram = 'Year_5' | [
"Please",
"list",
"the",
"IDs",
"of",
"the",
"advisors",
"of",
"the",
"students",
"who",
"are",
"in",
"the",
"5th",
"year",
"of",
"their",
"program",
"."
] | [
{
"id": 3,
"type": "column",
"value": "yearsinprogram"
},
{
"id": 0,
"type": "column",
"value": "p_id_dummy"
},
{
"id": 1,
"type": "table",
"value": "advisedby"
},
{
"id": 2,
"type": "table",
"value": "person"
},
{
"id": 4,
"type": "value",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
16,
17,
18
]
},
{
"entity_id": 4,
"token_idxs": [
15
]
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O"
] |
9,606 | perpetrator | spider:train_spider.json:2311 | What is the name of the perpetrator with the biggest weight. | SELECT T1.Name FROM people AS T1 JOIN perpetrator AS T2 ON T1.People_ID = T2.People_ID ORDER BY T1.Weight DESC LIMIT 1 | [
"What",
"is",
"the",
"name",
"of",
"the",
"perpetrator",
"with",
"the",
"biggest",
"weight",
"."
] | [
{
"id": 2,
"type": "table",
"value": "perpetrator"
},
{
"id": 4,
"type": "column",
"value": "people_id"
},
{
"id": 1,
"type": "table",
"value": "people"
},
{
"id": 3,
"type": "column",
"value": "weight"
},
{
"id": 0,
"type": "column",
"valu... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
9,607 | country_language | bird:test.json:1367 | Give the average justice scores across all countries. | SELECT avg(justice_score) FROM countries | [
"Give",
"the",
"average",
"justice",
"scores",
"across",
"all",
"countries",
"."
] | [
{
"id": 1,
"type": "column",
"value": "justice_score"
},
{
"id": 0,
"type": "table",
"value": "countries"
}
] | [
{
"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,
"toke... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O"
] |
9,608 | book_1 | bird:test.json:592 | What are the names of clients who have ordered Pride and Prejudice? | SELECT T3.name FROM Orders AS T1 JOIN Books_Order AS T2 ON T1.idOrder = T2.idOrder JOIN Client AS T3 ON T1.idClient = T3.idClient JOIN Book AS T4 ON T4.isbn = T2.isbn WHERE T4.title = "Pride and Prejudice" | [
"What",
"are",
"the",
"names",
"of",
"clients",
"who",
"have",
"ordered",
"Pride",
"and",
"Prejudice",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "Pride and Prejudice"
},
{
"id": 7,
"type": "table",
"value": "books_order"
},
{
"id": 8,
"type": "column",
"value": "idclient"
},
{
"id": 9,
"type": "column",
"value": "idorder"
},
{
"id": 4,
"type": "tabl... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
9,
10,
11
]
},
{
"entity_id": 4,
"token_idxs": [
5
]
},
{
"... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O"
] |
9,609 | e_learning | spider:train_spider.json:3786 | How many tests have result "Fail"? | SELECT count(*) FROM Student_Tests_Taken WHERE test_result = "Fail" | [
"How",
"many",
"tests",
"have",
"result",
"\"",
"Fail",
"\"",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "student_tests_taken"
},
{
"id": 1,
"type": "column",
"value": "test_result"
},
{
"id": 2,
"type": "column",
"value": "Fail"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
2,
3,
4
]
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"O",
"O"
] |
9,610 | college_3 | spider:train_spider.json:4705 | Find the names of departments that are either in division AS or in division EN and in Building NEB. | SELECT DName FROM DEPARTMENT WHERE Division = "AS" UNION SELECT DName FROM DEPARTMENT WHERE Division = "EN" AND Building = "NEB" | [
"Find",
"the",
"names",
"of",
"departments",
"that",
"are",
"either",
"in",
"division",
"AS",
"or",
"in",
"division",
"EN",
"and",
"in",
"Building",
"NEB",
"."
] | [
{
"id": 0,
"type": "table",
"value": "department"
},
{
"id": 2,
"type": "column",
"value": "division"
},
{
"id": 5,
"type": "column",
"value": "building"
},
{
"id": 1,
"type": "column",
"value": "dname"
},
{
"id": 6,
"type": "column",
"valu... | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
13
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": [
14
]
},
... | [
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O"
] |
9,611 | superhero | bird:dev.json:824 | Identify superheroes who can control wind and list their names in alphabetical order. | SELECT T1.superhero_name FROM superhero AS T1 INNER JOIN hero_power AS T2 ON T1.id = T2.hero_id INNER JOIN superpower AS T3 ON T2.power_id = T3.id WHERE T3.power_name = 'Wind Control' ORDER BY T1.superhero_name | [
"Identify",
"superheroes",
"who",
"can",
"control",
"wind",
"and",
"list",
"their",
"names",
"in",
"alphabetical",
"order",
"."
] | [
{
"id": 0,
"type": "column",
"value": "superhero_name"
},
{
"id": 3,
"type": "value",
"value": "Wind Control"
},
{
"id": 1,
"type": "table",
"value": "superpower"
},
{
"id": 2,
"type": "column",
"value": "power_name"
},
{
"id": 5,
"type": "tabl... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
3,
4
]
},
{
"entity_id": 4,
"token_idxs": [
1
]
},
{
"entity_id": 5,
"toke... | [
"O",
"B-TABLE",
"O",
"B-VALUE",
"I-VALUE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
9,612 | works_cycles | bird:train.json:7366 | What percentage of businesses in the Northwest US have forecasted annual sales of above 300,000? | SELECT CAST(SUM(CASE WHEN T1.SalesQuota > 300000 THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(T1.BusinessEntityID) FROM SalesPerson AS T1 INNER JOIN SalesTerritory AS T2 ON T1.TerritoryID = T2.TerritoryID WHERE T2.CountryRegionCode = 'US' AND T2.Name = 'Northwest' | [
"What",
"percentage",
"of",
"businesses",
"in",
"the",
"Northwest",
"US",
"have",
"forecasted",
"annual",
"sales",
"of",
"above",
"300,000",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "countryregioncode"
},
{
"id": 8,
"type": "column",
"value": "businessentityid"
},
{
"id": 1,
"type": "table",
"value": "salesterritory"
},
{
"id": 0,
"type": "table",
"value": "salesperson"
},
{
"id": 2,
"... | [
{
"entity_id": 0,
"token_idxs": [
12
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
7
]
},
{
"entity_id": 5,
"token_idxs":... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-VALUE",
"B-VALUE",
"O",
"O",
"O",
"B-COLUMN",
"B-TABLE",
"O",
"B-VALUE",
"O"
] |
9,613 | coinmarketcap | bird:train.json:6257 | What was the price of 1 Bitcoin in 2016? | SELECT AVG(T2.price) FROM coins AS T1 INNER JOIN historical AS T2 ON T1.id = T2.coin_id WHERE T1.name = 'Bitcoin' AND STRFTIME('%Y', T2.date) = '2016' | [
"What",
"was",
"the",
"price",
"of",
"1",
"Bitcoin",
"in",
"2016",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "historical"
},
{
"id": 4,
"type": "column",
"value": "coin_id"
},
{
"id": 6,
"type": "value",
"value": "Bitcoin"
},
{
"id": 0,
"type": "table",
"value": "coins"
},
{
"id": 2,
"type": "column",
"value": ... | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
7
]
},
{
"entity_id": 5,
"... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"B-VALUE",
"O"
] |
9,614 | disney | bird:train.json:4734 | Name the top 5 highest-grossing Disney movies adjusted for inflation. Identify the percentage they contributed to the total of Disney's current gross. | SELECT SUM(CASE WHEN CAST(REPLACE(trim(inflation_adjusted_gross, '$'), ',', '') AS REAL) > 1236035515 THEN CAST(REPLACE(trim(inflation_adjusted_gross, '$'), ',', '') AS REAL) ELSE 0 END) * 100 / SUM(CAST(REPLACE(trim(inflation_adjusted_gross, '$'), ',', '') AS REAL)) FROM movies_total_gross | [
"Name",
"the",
"top",
"5",
"highest",
"-",
"grossing",
"Disney",
"movies",
"adjusted",
"for",
"inflation",
".",
"Identify",
"the",
"percentage",
"they",
"contributed",
"to",
"the",
"total",
"of",
"Disney",
"'s",
"current",
"gross",
"."
] | [
{
"id": 4,
"type": "column",
"value": "inflation_adjusted_gross"
},
{
"id": 0,
"type": "table",
"value": "movies_total_gross"
},
{
"id": 6,
"type": "value",
"value": "1236035515"
},
{
"id": 1,
"type": "value",
"value": "100"
},
{
"id": 2,
"type... | [
{
"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... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
9,615 | book_1 | bird:test.json:540 | What is the title of the book that has been ordered the greatest number of times? | SELECT T2.title FROM Books_Order AS T1 JOIN Book AS T2 ON T1.isbn = T2.isbn GROUP BY T1.isbn ORDER BY count(*) DESC LIMIT 1 | [
"What",
"is",
"the",
"title",
"of",
"the",
"book",
"that",
"has",
"been",
"ordered",
"the",
"greatest",
"number",
"of",
"times",
"?"
] | [
{
"id": 2,
"type": "table",
"value": "books_order"
},
{
"id": 1,
"type": "column",
"value": "title"
},
{
"id": 0,
"type": "column",
"value": "isbn"
},
{
"id": 3,
"type": "table",
"value": "book"
}
] | [
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
6
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
9,616 | synthea | bird:train.json:1465 | From 7/9/2010 to 10/29/2013, how many black patients were immunized with the meningococcal MCV4P vaccine? | SELECT COUNT(DISTINCT T1.patient) FROM patients AS T1 INNER JOIN immunizations AS T2 ON T1.patient = T2.PATIENT WHERE T2.DESCRIPTION = 'meningococcal MCV4P' AND T2.DATE BETWEEN '2010-07-09' AND '2013-10-29' AND T1.race = 'black' | [
"From",
"7/9/2010",
"to",
"10/29/2013",
",",
"how",
"many",
"black",
"patients",
"were",
"immunized",
"with",
"the",
"meningococcal",
"MCV4P",
"vaccine",
"?"
] | [
{
"id": 4,
"type": "value",
"value": "meningococcal MCV4P"
},
{
"id": 1,
"type": "table",
"value": "immunizations"
},
{
"id": 3,
"type": "column",
"value": "description"
},
{
"id": 6,
"type": "value",
"value": "2010-07-09"
},
{
"id": 7,
"type":... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
10
]
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
13,
14
]
},
{
"entity_i... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O",
"O"
] |
9,617 | mental_health_survey | bird:train.json:4593 | What is the average result of the question "What is your age?" in 2014's survey? | SELECT CAST(SUM(T2.AnswerText) AS REAL) / COUNT(T2.UserID) AS "avg" FROM Question AS T1 INNER JOIN Answer AS T2 ON T1.questionid = T2.QuestionID WHERE T2.SurveyID = 2014 AND T1.questiontext LIKE 'What is your age?' | [
"What",
"is",
"the",
"average",
"result",
"of",
"the",
"question",
"\"",
"What",
"is",
"your",
"age",
"?",
"\"",
"in",
"2014",
"'s",
"survey",
"?"
] | [
{
"id": 6,
"type": "value",
"value": "What is your age?"
},
{
"id": 5,
"type": "column",
"value": "questiontext"
},
{
"id": 2,
"type": "column",
"value": "questionid"
},
{
"id": 8,
"type": "column",
"value": "answertext"
},
{
"id": 0,
"type": "... | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
18
]
},
{
"entity_id": 4,
"token_idxs": [
16
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"B-VALUE",
"O",
"B-COLUMN",
"O"
] |
9,618 | boat_1 | bird:test.json:883 | Find the name and age of the oldest sailor. | SELECT name , age FROM Sailors WHERE age = ( SELECT max(age) FROM Sailors ) | [
"Find",
"the",
"name",
"and",
"age",
"of",
"the",
"oldest",
"sailor",
"."
] | [
{
"id": 0,
"type": "table",
"value": "sailors"
},
{
"id": 1,
"type": "column",
"value": "name"
},
{
"id": 2,
"type": "column",
"value": "age"
}
] | [
{
"entity_id": 0,
"token_idxs": [
8
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
4
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
9,619 | authors | bird:train.json:3565 | Who are the co-authors for Jei Keon Chae and what is the title of paper written by them? | SELECT T2.AuthorId, T1.Title FROM Paper AS T1 INNER JOIN PaperAuthor AS T2 ON T1.Id = T2.PaperId WHERE T2.Name = 'Jei Keon Chae' | [
"Who",
"are",
"the",
"co",
"-",
"authors",
"for",
"Jei",
"Keon",
"Chae",
"and",
"what",
"is",
"the",
"title",
"of",
"paper",
"written",
"by",
"them",
"?"
] | [
{
"id": 5,
"type": "value",
"value": "Jei Keon Chae"
},
{
"id": 3,
"type": "table",
"value": "paperauthor"
},
{
"id": 0,
"type": "column",
"value": "authorid"
},
{
"id": 7,
"type": "column",
"value": "paperid"
},
{
"id": 1,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
14
]
},
{
"entity_id": 2,
"token_idxs": [
16
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"O"
] |
9,620 | climbing | spider:train_spider.json:1133 | What is the height of the mountain climbined by the climbing who had the most points? | SELECT T2.Height FROM climber AS T1 JOIN mountain AS T2 ON T1.Mountain_ID = T2.Mountain_ID ORDER BY T1.Points DESC LIMIT 1 | [
"What",
"is",
"the",
"height",
"of",
"the",
"mountain",
"climbined",
"by",
"the",
"climbing",
"who",
"had",
"the",
"most",
"points",
"?"
] | [
{
"id": 4,
"type": "column",
"value": "mountain_id"
},
{
"id": 2,
"type": "table",
"value": "mountain"
},
{
"id": 1,
"type": "table",
"value": "climber"
},
{
"id": 0,
"type": "column",
"value": "height"
},
{
"id": 3,
"type": "column",
"valu... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": [
15
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
9,621 | airline | bird:train.json:5840 | Provide the number of airplanes that landed on Oakland Airport on 2018/8/7. | SELECT SUM(CASE WHEN T1.Description LIKE '%Oakland%' THEN 1 ELSE 0 END) AS count FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.DEST WHERE T2.FL_DATE = '2018/8/7' | [
"Provide",
"the",
"number",
"of",
"airplanes",
"that",
"landed",
"on",
"Oakland",
"Airport",
"on",
"2018/8/7",
"."
] | [
{
"id": 8,
"type": "column",
"value": "description"
},
{
"id": 9,
"type": "value",
"value": "%Oakland%"
},
{
"id": 0,
"type": "table",
"value": "airports"
},
{
"id": 1,
"type": "table",
"value": "airlines"
},
{
"id": 3,
"type": "value",
"va... | [
{
"entity_id": 0,
"token_idxs": [
9
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
11
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-VALUE",
"B-TABLE",
"O",
"B-VALUE",
"O"
] |
9,622 | retail_complains | bird:train.json:283 | What is the medium through which most complaints are registered in Florida? | SELECT T3.`Submitted via` FROM callcenterlogs AS T1 INNER JOIN client AS T2 ON T1.`rand client` = T2.client_id INNER JOIN events AS T3 ON T1.`Complaint ID` = T3.`Complaint ID` WHERE T2.state = 'FL' GROUP BY T1.`Complaint ID` ORDER BY COUNT(T1.`Complaint ID`) DESC LIMIT 1 | [
"What",
"is",
"the",
"medium",
"through",
"which",
"most",
"complaints",
"are",
"registered",
"in",
"Florida",
"?"
] | [
{
"id": 5,
"type": "table",
"value": "callcenterlogs"
},
{
"id": 1,
"type": "column",
"value": "Submitted via"
},
{
"id": 0,
"type": "column",
"value": "Complaint ID"
},
{
"id": 7,
"type": "column",
"value": "rand client"
},
{
"id": 8,
"type": ... | [
{
"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... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O"
] |
9,623 | real_estate_rentals | bird:test.json:1445 | What are the buildings, streets, and cities corresponding to the addresses of senior citizens? | SELECT T1.line_1_number_building , T1.line_2_number_street , T1.town_city FROM Addresses AS T1 JOIN Users AS T2 ON T1.address_id = T2.user_address_id WHERE T2.user_category_code = 'Senior Citizen'; | [
"What",
"are",
"the",
"buildings",
",",
"streets",
",",
"and",
"cities",
"corresponding",
"to",
"the",
"addresses",
"of",
"senior",
"citizens",
"?"
] | [
{
"id": 0,
"type": "column",
"value": "line_1_number_building"
},
{
"id": 1,
"type": "column",
"value": "line_2_number_street"
},
{
"id": 5,
"type": "column",
"value": "user_category_code"
},
{
"id": 8,
"type": "column",
"value": "user_address_id"
},
{... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
12
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"I-VALUE",
"O"
] |
9,624 | customer_deliveries | spider:train_spider.json:2843 | Find the name and price of the product that has been ordered the greatest number of times. | SELECT t1.product_name , t1.product_price FROM products AS t1 JOIN regular_order_products AS t2 ON t1.product_id = t2.product_id GROUP BY t2.product_id ORDER BY count(*) DESC LIMIT 1 | [
"Find",
"the",
"name",
"and",
"price",
"of",
"the",
"product",
"that",
"has",
"been",
"ordered",
"the",
"greatest",
"number",
"of",
"times",
"."
] | [
{
"id": 4,
"type": "table",
"value": "regular_order_products"
},
{
"id": 2,
"type": "column",
"value": "product_price"
},
{
"id": 1,
"type": "column",
"value": "product_name"
},
{
"id": 0,
"type": "column",
"value": "product_id"
},
{
"id": 3,
"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
7
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
9,625 | chinook_1 | spider:train_spider.json:828 | What are the names of tracks that contain the the word you in them? | SELECT Name FROM TRACK WHERE Name LIKE '%you%' | [
"What",
"are",
"the",
"names",
"of",
"tracks",
"that",
"contain",
"the",
"the",
"word",
"you",
"in",
"them",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "track"
},
{
"id": 2,
"type": "value",
"value": "%you%"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O"
] |
9,626 | food_inspection_2 | bird:train.json:6175 | How many inspections failed in 2014? | SELECT COUNT(inspection_id) FROM inspection WHERE strftime('%Y', inspection_date) = '2014' AND results = 'Fail' | [
"How",
"many",
"inspections",
"failed",
"in",
"2014",
"?"
] | [
{
"id": 6,
"type": "column",
"value": "inspection_date"
},
{
"id": 1,
"type": "column",
"value": "inspection_id"
},
{
"id": 0,
"type": "table",
"value": "inspection"
},
{
"id": 3,
"type": "column",
"value": "results"
},
{
"id": 2,
"type": "valu... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
3
]
},
{
"entity_id": 5,
"... | [
"O",
"O",
"B-TABLE",
"B-VALUE",
"O",
"B-VALUE",
"O"
] |
9,627 | art_1 | bird:test.json:1201 | What is the name of the scuplture that was created most recently ? | select title from sculptures order by year desc limit 1 | [
"What",
"is",
"the",
"name",
"of",
"the",
"scuplture",
"that",
"was",
"created",
"most",
"recently",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "sculptures"
},
{
"id": 1,
"type": "column",
"value": "title"
},
{
"id": 2,
"type": "column",
"value": "year"
}
] | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"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": []
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O"
] |
9,628 | cookbook | bird:train.json:8911 | Provide the ingredients and maximum quantities of the recipe which can serve 7 people. | SELECT T3.name, T2.max_qty 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 T1.servings = 7 | [
"Provide",
"the",
"ingredients",
"and",
"maximum",
"quantities",
"of",
"the",
"recipe",
"which",
"can",
"serve",
"7",
"people",
"."
] | [
{
"id": 7,
"type": "column",
"value": "ingredient_id"
},
{
"id": 2,
"type": "table",
"value": "ingredient"
},
{
"id": 8,
"type": "column",
"value": "recipe_id"
},
{
"id": 3,
"type": "column",
"value": "servings"
},
{
"id": 6,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
2
]
},
{
"entity_id": 3,
"token_idxs": [
11
]
},
{
"entity_id": 4,
"token_idxs": [
12
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"B-VALUE",
"O",
"O"
] |
9,629 | citeseer | bird:train.json:4151 | Name the paper which is cited most times and the paper which is cited least times? Also, find the number of times each one is cited. | SELECT cited_paper_id, COUNT(cited_paper_id), ( SELECT cited_paper_id FROM cites GROUP BY cited_paper_id ORDER BY COUNT(cited_paper_id) ASC LIMIT 1 ), ( SELECT COUNT(cited_paper_id) FROM cites GROUP BY cited_paper_id ORDER BY COUNT(cited_paper_id) ASC LIMIT 1 ) FROM cites GROUP BY cited_paper_id ORDER BY COUNT(cited_pa... | [
"Name",
"the",
"paper",
"which",
"is",
"cited",
"most",
"times",
"and",
"the",
"paper",
"which",
"is",
"cited",
"least",
"times",
"?",
"Also",
",",
"find",
"the",
"number",
"of",
"times",
"each",
"one",
"is",
"cited",
"."
] | [
{
"id": 1,
"type": "column",
"value": "cited_paper_id"
},
{
"id": 0,
"type": "table",
"value": "cites"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
1,
2
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"toke... | [
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.