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 |
|---|---|---|---|---|---|---|---|---|
15,632 | image_and_language | bird:train.json:7543 | State the explanation about object class 10. | SELECT OBJ_CLASS FROM OBJ_CLASSES WHERE OBJ_CLASS_ID = 10 | [
"State",
"the",
"explanation",
"about",
"object",
"class",
"10",
"."
] | [
{
"id": 2,
"type": "column",
"value": "obj_class_id"
},
{
"id": 0,
"type": "table",
"value": "obj_classes"
},
{
"id": 1,
"type": "column",
"value": "obj_class"
},
{
"id": 3,
"type": "value",
"value": "10"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
4,
5
]
},
{
"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-COLUMN",
"I-COLUMN",
"B-VALUE",
"O"
] |
15,633 | movies_4 | bird:train.json:412 | What is the name of the director of photography of the movie "Pirates of the Caribbean: At World's End"? | SELECT T3.person_name FROM movie AS T1 INNER JOIN movie_crew AS T2 ON T1.movie_id = T2.movie_id INNER JOIN person AS T3 ON T2.person_id = T3.person_id WHERE T1.title LIKE 'Pirates of the Caribbean: At World%s End' AND T2.job = 'Director of Photography' | [
"What",
"is",
"the",
"name",
"of",
"the",
"director",
"of",
"photography",
"of",
"the",
"movie",
"\"",
"Pirates",
"of",
"the",
"Caribbean",
":",
"At",
"World",
"'s",
"End",
"\"",
"?"
] | [
{
"id": 6,
"type": "value",
"value": "Pirates of the Caribbean: At World%s End"
},
{
"id": 8,
"type": "value",
"value": "Director of Photography"
},
{
"id": 0,
"type": "column",
"value": "person_name"
},
{
"id": 3,
"type": "table",
"value": "movie_crew"
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O"
] |
15,634 | flight_1 | spider:train_spider.json:376 | What is the name of the 3 employees who get paid the least? | SELECT name FROM Employee ORDER BY salary ASC LIMIT 3 | [
"What",
"is",
"the",
"name",
"of",
"the",
"3",
"employees",
"who",
"get",
"paid",
"the",
"least",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "employee"
},
{
"id": 2,
"type": "column",
"value": "salary"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O"
] |
15,635 | language_corpus | bird:train.json:5745 | Please list any three Wikipedia pages that are written in Catalan, together with their titles and revision page numbers. | SELECT title, revision FROM pages WHERE lid = 1 LIMIT 3 | [
"Please",
"list",
"any",
"three",
"Wikipedia",
"pages",
"that",
"are",
"written",
"in",
"Catalan",
",",
"together",
"with",
"their",
"titles",
"and",
"revision",
"page",
"numbers",
"."
] | [
{
"id": 2,
"type": "column",
"value": "revision"
},
{
"id": 0,
"type": "table",
"value": "pages"
},
{
"id": 1,
"type": "column",
"value": "title"
},
{
"id": 3,
"type": "column",
"value": "lid"
},
{
"id": 4,
"type": "value",
"value": "1"
}... | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
15
]
},
{
"entity_id": 2,
"token_idxs": [
17
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"O"
] |
15,636 | ship_1 | spider:train_spider.json:6247 | What is the name, type, and flag of the ship that was built in the most recent year? | SELECT name , TYPE , flag FROM ship ORDER BY built_year DESC LIMIT 1 | [
"What",
"is",
"the",
"name",
",",
"type",
",",
"and",
"flag",
"of",
"the",
"ship",
"that",
"was",
"built",
"in",
"the",
"most",
"recent",
"year",
"?"
] | [
{
"id": 4,
"type": "column",
"value": "built_year"
},
{
"id": 0,
"type": "table",
"value": "ship"
},
{
"id": 1,
"type": "column",
"value": "name"
},
{
"id": 2,
"type": "column",
"value": "type"
},
{
"id": 3,
"type": "column",
"value": "flag... | [
{
"entity_id": 0,
"token_idxs": [
11
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": [
14
]
},
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O"
] |
15,637 | donor | bird:train.json:3267 | How many schools in Suffolk County have Ph.D. teachers? | SELECT COUNT(schoolid) FROM projects WHERE teacher_prefix = 'Dr.' AND school_county = 'Suffolk' | [
"How",
"many",
"schools",
"in",
"Suffolk",
"County",
"have",
"Ph.D.",
"teachers",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "teacher_prefix"
},
{
"id": 4,
"type": "column",
"value": "school_county"
},
{
"id": 0,
"type": "table",
"value": "projects"
},
{
"id": 1,
"type": "column",
"value": "schoolid"
},
{
"id": 5,
"type": "value"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
5
]
},
{
"entity_id": 5,
"... | [
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O"
] |
15,638 | food_inspection | bird:train.json:8808 | How many owners have 5 or more establishments? | SELECT COUNT(T1.owner_name) FROM ( SELECT owner_name FROM businesses GROUP BY owner_name HAVING COUNT(owner_name) > 5 ) T1 | [
"How",
"many",
"owners",
"have",
"5",
"or",
"more",
"establishments",
"?"
] | [
{
"id": 0,
"type": "column",
"value": "owner_name"
},
{
"id": 1,
"type": "table",
"value": "businesses"
},
{
"id": 2,
"type": "value",
"value": "5"
}
] | [
{
"entity_id": 0,
"token_idxs": [
2,
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
4
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"toke... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"B-VALUE",
"O",
"O",
"O",
"O"
] |
15,639 | movies_4 | bird:train.json:417 | In how many movie does Dariusz Wolski work as the director of photography? | SELECT COUNT(T2.movie_id) FROM person AS T1 INNER JOIN movie_crew AS T2 ON T1.person_id = T2.person_id WHERE T1.person_name = 'Dariusz Wolski' AND T2.job = 'Director of Photography' | [
"In",
"how",
"many",
"movie",
"does",
"Dariusz",
"Wolski",
"work",
"as",
"the",
"director",
"of",
"photography",
"?"
] | [
{
"id": 7,
"type": "value",
"value": "Director of Photography"
},
{
"id": 5,
"type": "value",
"value": "Dariusz Wolski"
},
{
"id": 4,
"type": "column",
"value": "person_name"
},
{
"id": 1,
"type": "table",
"value": "movie_crew"
},
{
"id": 3,
"t... | [
{
"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": []
},
{
"entity_id": 5,
"token_idxs": [
5,
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O"
] |
15,640 | twitter_1 | spider:train_spider.json:299 | Find the average number of followers for the users who had some tweets. | SELECT avg(followers) FROM user_profiles WHERE UID IN (SELECT UID FROM tweets) | [
"Find",
"the",
"average",
"number",
"of",
"followers",
"for",
"the",
"users",
"who",
"had",
"some",
"tweets",
"."
] | [
{
"id": 0,
"type": "table",
"value": "user_profiles"
},
{
"id": 2,
"type": "column",
"value": "followers"
},
{
"id": 3,
"type": "table",
"value": "tweets"
},
{
"id": 1,
"type": "column",
"value": "uid"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": [
12
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs":... | [
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
15,641 | card_games | bird:dev.json:389 | List down the name of cards with original types of Creature - Elf and the date of rulings for these cards. | SELECT T1.id, T2.date FROM cards AS T1 INNER JOIN rulings AS T2 ON T1.uuid = T2.uuid WHERE T1.originalType = 'Creature - Elf' | [
"List",
"down",
"the",
"name",
"of",
"cards",
"with",
"original",
"types",
"of",
"Creature",
"-",
"Elf",
"and",
"the",
"date",
"of",
"rulings",
"for",
"these",
"cards",
"."
] | [
{
"id": 5,
"type": "value",
"value": "Creature - Elf"
},
{
"id": 4,
"type": "column",
"value": "originaltype"
},
{
"id": 3,
"type": "table",
"value": "rulings"
},
{
"id": 2,
"type": "table",
"value": "cards"
},
{
"id": 1,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
15
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": [
17
]
},
{
"entity_id": 4,
"token_idxs": [
7,
8
]
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O",
"O",
"O"
] |
15,642 | tracking_grants_for_research | spider:train_spider.json:4343 | When did researchers start and stop working? | SELECT date_from , date_to FROM Project_Staff WHERE role_code = 'researcher' | [
"When",
"did",
"researchers",
"start",
"and",
"stop",
"working",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "project_staff"
},
{
"id": 4,
"type": "value",
"value": "researcher"
},
{
"id": 1,
"type": "column",
"value": "date_from"
},
{
"id": 3,
"type": "column",
"value": "role_code"
},
{
"id": 2,
"type": "column",
... | [
{
"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": [
2
]
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O"
] |
15,643 | manufactory_1 | spider:train_spider.json:5319 | What are the names of companies that do not make DVD drives? | SELECT name FROM manufacturers EXCEPT SELECT T2.name FROM products AS T1 JOIN manufacturers AS T2 ON T1.Manufacturer = T2.code WHERE T1.name = 'DVD drive' | [
"What",
"are",
"the",
"names",
"of",
"companies",
"that",
"do",
"not",
"make",
"DVD",
"drives",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "manufacturers"
},
{
"id": 4,
"type": "column",
"value": "manufacturer"
},
{
"id": 3,
"type": "value",
"value": "DVD drive"
},
{
"id": 2,
"type": "table",
"value": "products"
},
{
"id": 1,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
10,
11
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"to... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O"
] |
15,644 | music_tracker | bird:train.json:2082 | In 1980, how many singles were released by sugar daddy? | SELECT COUNT(releaseType) FROM torrents WHERE artist LIKE 'sugar daddy' AND releaseType LIKE 'Single' AND groupYear = 1980 | [
"In",
"1980",
",",
"how",
"many",
"singles",
"were",
"released",
"by",
"sugar",
"daddy",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "releasetype"
},
{
"id": 3,
"type": "value",
"value": "sugar daddy"
},
{
"id": 5,
"type": "column",
"value": "groupyear"
},
{
"id": 0,
"type": "table",
"value": "torrents"
},
{
"id": 2,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
9,
10
]
},
{
"entity_id": 4,
"token_idxs": [
5
]
},
{
"entity_id"... | [
"O",
"B-VALUE",
"O",
"O",
"O",
"B-VALUE",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"O"
] |
15,645 | apartment_rentals | spider:train_spider.json:1199 | Give me a list of all the distinct building descriptions. | SELECT DISTINCT building_description FROM Apartment_Buildings | [
"Give",
"me",
"a",
"list",
"of",
"all",
"the",
"distinct",
"building",
"descriptions",
"."
] | [
{
"id": 1,
"type": "column",
"value": "building_description"
},
{
"id": 0,
"type": "table",
"value": "apartment_buildings"
}
] | [
{
"entity_id": 0,
"token_idxs": [
7,
8
]
},
{
"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",
"O",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"B-COLUMN",
"O"
] |
15,646 | video_games | bird:train.json:3373 | Provide the game publisher's name of the game with sales greater than 90% of the average sales in Japan. | SELECT DISTINCT T5.publisher_name FROM region AS T1 INNER JOIN region_sales AS T2 ON T1.id = T2.region_id INNER JOIN game_platform AS T3 ON T2.game_platform_id = T3.id INNER JOIN game_publisher AS T4 ON T3.game_publisher_id = T4.id INNER JOIN publisher AS T5 ON T4.publisher_id = T5.id WHERE T2.num_sales * 10000000 > ( SELECT AVG(T2.num_sales) * 100000 * 90 FROM region AS T1 INNER JOIN region_sales AS T2 ON T1.id = T2.region_id WHERE T1.region_name = 'Japan' ) | [
"Provide",
"the",
"game",
"publisher",
"'s",
"name",
"of",
"the",
"game",
"with",
"sales",
"greater",
"than",
"90",
"%",
"of",
"the",
"average",
"sales",
"in",
"Japan",
"."
] | [
{
"id": 8,
"type": "column",
"value": "game_publisher_id"
},
{
"id": 11,
"type": "column",
"value": "game_platform_id"
},
{
"id": 0,
"type": "column",
"value": "publisher_name"
},
{
"id": 2,
"type": "table",
"value": "game_publisher"
},
{
"id": 7,
... | [
{
"entity_id": 0,
"token_idxs": [
4,
5
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
2
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id":... | [
"O",
"O",
"B-TABLE",
"B-TABLE",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"O"
] |
15,647 | language_corpus | bird:train.json:5726 | Calculate the average number of different words that appear on all pages whose title begins with A. | SELECT AVG(words) FROM pages WHERE title LIKE 'A%' | [
"Calculate",
"the",
"average",
"number",
"of",
"different",
"words",
"that",
"appear",
"on",
"all",
"pages",
"whose",
"title",
"begins",
"with",
"A."
] | [
{
"id": 0,
"type": "table",
"value": "pages"
},
{
"id": 1,
"type": "column",
"value": "title"
},
{
"id": 3,
"type": "column",
"value": "words"
},
{
"id": 2,
"type": "value",
"value": "A%"
}
] | [
{
"entity_id": 0,
"token_idxs": [
11
]
},
{
"entity_id": 1,
"token_idxs": [
13
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
6
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"O"
] |
15,648 | thrombosis_prediction | bird:dev.json:1208 | Provide IDs for male patients with ALT glutamic pylvic transaminase (GPT) that have history of ALT glutamic pylvic transaminase (GPT) exceed the normal range. | SELECT DISTINCT T1.ID FROM Patient AS T1 INNER JOIN Laboratory AS T2 ON T1.ID = T2.ID WHERE T1.SEX = 'M' AND T2.GPT >= 60 | [
"Provide",
"IDs",
"for",
"male",
"patients",
"with",
"ALT",
"glutamic",
"pylvic",
"transaminase",
"(",
"GPT",
")",
"that",
"have",
"history",
"of",
"ALT",
"glutamic",
"pylvic",
"transaminase",
"(",
"GPT",
")",
"exceed",
"the",
"normal",
"range",
"."
] | [
{
"id": 2,
"type": "table",
"value": "laboratory"
},
{
"id": 1,
"type": "table",
"value": "patient"
},
{
"id": 3,
"type": "column",
"value": "sex"
},
{
"id": 5,
"type": "column",
"value": "gpt"
},
{
"id": 0,
"type": "column",
"value": "id"
... | [
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
15,649 | scientist_1 | spider:train_spider.json:6488 | What is the sum of hours for projects that scientists with the name Michael Rogers or Carol Smith are assigned to? | SELECT sum(T2.hours) FROM assignedto AS T1 JOIN projects AS T2 ON T1.project = T2.code JOIN scientists AS T3 ON T1.scientist = T3.SSN WHERE T3.name = 'Michael Rogers' OR T3.name = 'Carol Smith' | [
"What",
"is",
"the",
"sum",
"of",
"hours",
"for",
"projects",
"that",
"scientists",
"with",
"the",
"name",
"Michael",
"Rogers",
"or",
"Carol",
"Smith",
"are",
"assigned",
"to",
"?"
] | [
{
"id": 7,
"type": "value",
"value": "Michael Rogers"
},
{
"id": 8,
"type": "value",
"value": "Carol Smith"
},
{
"id": 0,
"type": "table",
"value": "scientists"
},
{
"id": 2,
"type": "table",
"value": "assignedto"
},
{
"id": 4,
"type": "column"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
19,
20
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
9
]
},
{
"entity_id... | [
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"B-VALUE",
"I-VALUE",
"O",
"B-VALUE",
"I-VALUE",
"O",
"B-TABLE",
"I-TABLE",
"O"
] |
15,650 | public_review_platform | bird:train.json:3913 | Find out which hotel and travel business having the most review? Calculate the standard deviation of the review star for this business. | SELECT T2.category_id FROM Business_Categories AS T1 INNER JOIN Categories AS T2 ON T1.category_id = T2.category_id INNER JOIN Reviews AS T3 ON T3.business_id = T1.business_id WHERE T2.category_name = 'Hotels & Travel' GROUP BY T2.category_id ORDER BY COUNT(T2.category_id) DESC LIMIT 1 | [
"Find",
"out",
"which",
"hotel",
"and",
"travel",
"business",
"having",
"the",
"most",
"review",
"?",
"Calculate",
"the",
"standard",
"deviation",
"of",
"the",
"review",
"star",
"for",
"this",
"business",
"."
] | [
{
"id": 4,
"type": "table",
"value": "business_categories"
},
{
"id": 3,
"type": "value",
"value": "Hotels & Travel"
},
{
"id": 2,
"type": "column",
"value": "category_name"
},
{
"id": 0,
"type": "column",
"value": "category_id"
},
{
"id": 6,
"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
10
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
3,
4,
5
]
},
{
"entity_id": 4,
"token_idxs": [
7,
8,
9
... | [
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"B-COLUMN",
"B-TABLE",
"I-TABLE",
"I-TABLE",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
15,651 | movie_1 | spider:train_spider.json:2517 | What are the ids of all moviest hat have not been reviewed by Britanny Harris? | SELECT mID FROM Rating EXCEPT SELECT T1.mID FROM Rating AS T1 JOIN Reviewer AS T2 ON T1.rID = T2.rID WHERE T2.name = "Brittany Harris" | [
"What",
"are",
"the",
"ids",
"of",
"all",
"moviest",
"hat",
"have",
"not",
"been",
"reviewed",
"by",
"Britanny",
"Harris",
"?"
] | [
{
"id": 4,
"type": "column",
"value": "Brittany Harris"
},
{
"id": 2,
"type": "table",
"value": "reviewer"
},
{
"id": 0,
"type": "table",
"value": "rating"
},
{
"id": 3,
"type": "column",
"value": "name"
},
{
"id": 1,
"type": "column",
"val... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
13,
14
]
},
{
"entity_id": 5,
"t... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"I-COLUMN",
"O"
] |
15,652 | menu | bird:train.json:5536 | Write down the image ID, full height, and full width of the menu that were used in the "100TH ANNIVERSARY OF BIRTH OF DANIEL WEBSTER" event. | SELECT T1.image_id, T1.full_height, T1.full_width FROM MenuPage AS T1 INNER JOIN Menu AS T2 ON T2.id = T1.menu_id WHERE T2.event = '100TH ANNIVERSARY OF BIRTH OF DANIEL WEBSTER' | [
"Write",
"down",
"the",
"image",
"ID",
",",
"full",
"height",
",",
"and",
"full",
"width",
"of",
"the",
"menu",
"that",
"were",
"used",
"in",
"the",
"\"",
"100TH",
"ANNIVERSARY",
"OF",
"BIRTH",
"OF",
"DANIEL",
"WEBSTER",
"\"",
"event",
"."
] | [
{
"id": 6,
"type": "value",
"value": "100TH ANNIVERSARY OF BIRTH OF DANIEL WEBSTER"
},
{
"id": 1,
"type": "column",
"value": "full_height"
},
{
"id": 2,
"type": "column",
"value": "full_width"
},
{
"id": 0,
"type": "column",
"value": "image_id"
},
{
... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
6,
7
]
},
{
"entity_id": 2,
"token_idxs": [
10,
11
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
14
]
... | [
"O",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"B-COLUMN",
"O"
] |
15,653 | university | bird:train.json:8002 | What are the names of the criteria under Center for World University Rankings? | SELECT T2.criteria_name FROM ranking_system AS T1 INNER JOIN ranking_criteria AS T2 ON T1.id = T2.ranking_system_id WHERE T1.system_name = 'Center for World University Rankings' | [
"What",
"are",
"the",
"names",
"of",
"the",
"criteria",
"under",
"Center",
"for",
"World",
"University",
"Rankings",
"?"
] | [
{
"id": 4,
"type": "value",
"value": "Center for World University Rankings"
},
{
"id": 6,
"type": "column",
"value": "ranking_system_id"
},
{
"id": 2,
"type": "table",
"value": "ranking_criteria"
},
{
"id": 1,
"type": "table",
"value": "ranking_system"
}... | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
12
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
2,
3
]
},
{
"entity_id": 4,
"token_idxs": [
8,
9,
1... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"B-TABLE",
"O"
] |
15,654 | sales | bird:train.json:5387 | Calculate the total price of products purchased by Adam. | SELECT SUM(T3.Price * T2.quantity) FROM Customers AS T1 INNER JOIN Sales AS T2 ON T1.CustomerID = T2.CustomerID INNER JOIN Products AS T3 ON T2.ProductID = T3.ProductID WHERE T1.FirstName = 'Adam' | [
"Calculate",
"the",
"total",
"price",
"of",
"products",
"purchased",
"by",
"Adam",
"."
] | [
{
"id": 8,
"type": "column",
"value": "customerid"
},
{
"id": 1,
"type": "column",
"value": "firstname"
},
{
"id": 3,
"type": "table",
"value": "customers"
},
{
"id": 5,
"type": "column",
"value": "productid"
},
{
"id": 0,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
8
]
},
{
"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",
"B-VALUE",
"O"
] |
15,655 | regional_sales | bird:train.json:2738 | What was the best discount applied to sales orders in 2020? | SELECT MAX(`Discount Applied`) FROM `Sales Orders` WHERE OrderDate LIKE '%/%/20' | [
"What",
"was",
"the",
"best",
"discount",
"applied",
"to",
"sales",
"orders",
"in",
"2020",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "Discount Applied"
},
{
"id": 0,
"type": "table",
"value": "Sales Orders"
},
{
"id": 1,
"type": "column",
"value": "orderdate"
},
{
"id": 2,
"type": "value",
"value": "%/%/20"
}
] | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
4,
5
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id":... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O"
] |
15,656 | olympics | bird:train.json:5052 | How many athletes took part in the Olympic games held in Barcelona? | SELECT COUNT(T1.person_id) FROM games_competitor AS T1 INNER JOIN games_city AS T2 ON T1.games_id = T2.games_id INNER JOIN city AS T3 ON T2.city_id = T3.id WHERE T3.city_name = 'Barcelona' | [
"How",
"many",
"athletes",
"took",
"part",
"in",
"the",
"Olympic",
"games",
"held",
"in",
"Barcelona",
"?"
] | [
{
"id": 4,
"type": "table",
"value": "games_competitor"
},
{
"id": 5,
"type": "table",
"value": "games_city"
},
{
"id": 1,
"type": "column",
"value": "city_name"
},
{
"id": 2,
"type": "value",
"value": "Barcelona"
},
{
"id": 3,
"type": "column"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O"
] |
15,657 | public_review_platform | bird:train.json:4000 | List the business located in Mesa that have alcohol attribute. | SELECT T1.business_id FROM Business AS T1 INNER JOIN Business_Attributes AS T2 ON T1.business_id = T2.business_id INNER JOIN Attributes AS T3 ON T2.attribute_id = T3.attribute_id WHERE T1.city = 'Mesa' AND T3.attribute_name = 'Alcohol' | [
"List",
"the",
"business",
"located",
"in",
"Mesa",
"that",
"have",
"alcohol",
"attribute",
"."
] | [
{
"id": 3,
"type": "table",
"value": "business_attributes"
},
{
"id": 7,
"type": "column",
"value": "attribute_name"
},
{
"id": 4,
"type": "column",
"value": "attribute_id"
},
{
"id": 0,
"type": "column",
"value": "business_id"
},
{
"id": 1,
"t... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
9
]
},
{
"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",
"B-VALUE",
"O",
"O",
"B-VALUE",
"B-TABLE",
"O"
] |
15,658 | superstore | bird:train.json:2427 | In which segment does the customer who purchased the product from the east superstore with the highest original price belong? | SELECT T2.Segment FROM east_superstore AS T1 INNER JOIN people AS T2 ON T1.`Customer ID` = T2.`Customer ID` INNER JOIN product AS T3 ON T3.`Product ID` = T1.`Product ID` WHERE T1.Region = 'East' ORDER BY (T1.Sales / (1 - T1.Discount)) DESC LIMIT 1 | [
"In",
"which",
"segment",
"does",
"the",
"customer",
"who",
"purchased",
"the",
"product",
"from",
"the",
"east",
"superstore",
"with",
"the",
"highest",
"original",
"price",
"belong",
"?"
] | [
{
"id": 4,
"type": "table",
"value": "east_superstore"
},
{
"id": 8,
"type": "column",
"value": "Customer ID"
},
{
"id": 6,
"type": "column",
"value": "Product ID"
},
{
"id": 10,
"type": "column",
"value": "discount"
},
{
"id": 0,
"type": "colu... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
9
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
12
]
},
{
"entity_id": 4,
"token_idxs": [
13
]
},
{
"entit... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
15,659 | medicine_enzyme_interaction | spider:train_spider.json:976 | How many medicines are offered by each trade name? | SELECT trade_name , count(*) FROM medicine GROUP BY trade_name | [
"How",
"many",
"medicines",
"are",
"offered",
"by",
"each",
"trade",
"name",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "trade_name"
},
{
"id": 0,
"type": "table",
"value": "medicine"
}
] | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
7,
8
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"toke... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O"
] |
15,660 | retails | bird:train.json:6833 | What are the clerks of orders with line items shipped by mail? | SELECT T1.o_clerk FROM orders AS T1 INNER JOIN lineitem AS T2 ON T1.o_orderkey = T2.l_orderkey WHERE T2.l_shipmode = 'MAIL' | [
"What",
"are",
"the",
"clerks",
"of",
"orders",
"with",
"line",
"items",
"shipped",
"by",
"mail",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "l_shipmode"
},
{
"id": 5,
"type": "column",
"value": "o_orderkey"
},
{
"id": 6,
"type": "column",
"value": "l_orderkey"
},
{
"id": 2,
"type": "table",
"value": "lineitem"
},
{
"id": 0,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
7,
8
]
},
{
"entity_id": 3,
"token_idxs": [
9
]
},
{
"entity_id": 4,
"token_idxs": [
11
... | [
"O",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"B-TABLE",
"O",
"B-TABLE",
"I-TABLE",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
15,661 | e_commerce | bird:test.json:109 | What is the date of all orders that have been placed by customers with at least 2 payment methods? | SELECT date_order_placed FROM Orders WHERE customer_id IN ( SELECT T1.customer_id FROM Customers AS T1 JOIN Customer_Payment_Methods AS T2 ON T1.customer_id = T2.customer_id GROUP BY T1.customer_id HAVING count(*) >= 2 ) | [
"What",
"is",
"the",
"date",
"of",
"all",
"orders",
"that",
"have",
"been",
"placed",
"by",
"customers",
"with",
"at",
"least",
"2",
"payment",
"methods",
"?"
] | [
{
"id": 4,
"type": "table",
"value": "customer_payment_methods"
},
{
"id": 1,
"type": "column",
"value": "date_order_placed"
},
{
"id": 2,
"type": "column",
"value": "customer_id"
},
{
"id": 3,
"type": "table",
"value": "customers"
},
{
"id": 0,
... | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
12
]
},
{
"entity_id": 4,
"token_idxs": [
17,
18
]
},
{
"entity_i... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-VALUE",
"B-TABLE",
"I-TABLE",
"O"
] |
15,662 | social_media | bird:train.json:851 | Among all the tweets sent by male users in Argentina, what is the text of the one with the most number of likes? | SELECT T2.text FROM user AS T1 INNER JOIN twitter AS T2 ON T1.UserID = T2.UserID INNER JOIN location AS T3 ON T2.LocationID = T3.LocationID WHERE T3.Country = 'Argentina' AND T1.Gender = 'Male' ORDER BY T2.Likes DESC LIMIT 1 | [
"Among",
"all",
"the",
"tweets",
"sent",
"by",
"male",
"users",
"in",
"Argentina",
",",
"what",
"is",
"the",
"text",
"of",
"the",
"one",
"with",
"the",
"most",
"number",
"of",
"likes",
"?"
] | [
{
"id": 4,
"type": "column",
"value": "locationid"
},
{
"id": 6,
"type": "value",
"value": "Argentina"
},
{
"id": 1,
"type": "table",
"value": "location"
},
{
"id": 3,
"type": "table",
"value": "twitter"
},
{
"id": 5,
"type": "column",
"val... | [
{
"entity_id": 0,
"token_idxs": [
14
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
23
]
},
{
"entity_id": 3,
"token_idxs": [
18,
19
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
15,663 | tracking_software_problems | spider:train_spider.json:5385 | Which problems were reported by the staff named Dameon Frami or Jolie Weber? Give me the ids of the problems. | SELECT product_id FROM problems AS T1 JOIN staff AS T2 ON T1.reported_by_staff_id = T2.staff_id WHERE T2.staff_first_name = "Dameon" AND T2.staff_last_name = "Frami" UNION SELECT product_id FROM problems AS T1 JOIN staff AS T2 ON T1.reported_by_staff_id = T2.staff_id WHERE T2.staff_first_name = "Jolie" AND T2.staff_last_name = "Weber" | [
"Which",
"problems",
"were",
"reported",
"by",
"the",
"staff",
"named",
"Dameon",
"Frami",
"or",
"Jolie",
"Weber",
"?",
"Give",
"me",
"the",
"ids",
"of",
"the",
"problems",
"."
] | [
{
"id": 3,
"type": "column",
"value": "reported_by_staff_id"
},
{
"id": 5,
"type": "column",
"value": "staff_first_name"
},
{
"id": 7,
"type": "column",
"value": "staff_last_name"
},
{
"id": 0,
"type": "column",
"value": "product_id"
},
{
"id": 1,
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
20
]
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": [
3,
4,
5
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"e... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"B-TABLE",
"B-COLUMN",
"B-COLUMN",
"B-COLUMN",
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
15,664 | airline | bird:train.json:5878 | How many flights from Charlotte Douglas International Airport to Austin - Bergstrom International Airport experienced serious reasons that cause flight cancellation? | SELECT COUNT(*) FROM Airlines AS T1 INNER JOIN Airports AS T2 ON T1.ORIGIN = T2.Code WHERE T1.ORIGIN = 'CLT' AND T1.DEST = 'AUS' AND T2.Description = 'Charlotte, NC: Charlotte Douglas International' AND T1.CANCELLATION_CODE = 'A' | [
"How",
"many",
"flights",
"from",
"Charlotte",
"Douglas",
"International",
"Airport",
"to",
"Austin",
"-",
"Bergstrom",
"International",
"Airport",
"experienced",
"serious",
"reasons",
"that",
"cause",
"flight",
"cancellation",
"?"
] | [
{
"id": 8,
"type": "value",
"value": "Charlotte, NC: Charlotte Douglas International"
},
{
"id": 9,
"type": "column",
"value": "cancellation_code"
},
{
"id": 7,
"type": "column",
"value": "description"
},
{
"id": 0,
"type": "table",
"value": "airlines"
}... | [
{
"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",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"B-COLUMN",
"O"
] |
15,665 | art_1 | bird:test.json:1261 | What is the full name of the artist with a sculpture whose title includes the word "female"? | SELECT T1.lname , T1.fname FROM artists AS T1 JOIN sculptures AS T2 ON T1.artistID = T2.sculptorID WHERE T2.title LIKE "%female%" | [
"What",
"is",
"the",
"full",
"name",
"of",
"the",
"artist",
"with",
"a",
"sculpture",
"whose",
"title",
"includes",
"the",
"word",
"\"",
"female",
"\"",
"?"
] | [
{
"id": 3,
"type": "table",
"value": "sculptures"
},
{
"id": 7,
"type": "column",
"value": "sculptorid"
},
{
"id": 5,
"type": "column",
"value": "%female%"
},
{
"id": 6,
"type": "column",
"value": "artistid"
},
{
"id": 2,
"type": "table",
"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
12
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O"
] |
15,666 | mental_health_survey | bird:train.json:4611 | What was the percentage for the answer of "Yes" was given to the question "Has your employer ever formally discussed mental health (for example, as part of a wellness campaign or other official communication)?"? | SELECT CAST(SUM(CASE WHEN T1.AnswerText LIKE 'Yes' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(T1.UserID) FROM Answer AS T1 INNER JOIN Question AS T2 ON T1.QuestionID = T2.questionid WHERE T2.questiontext LIKE 'Has your employer ever formally discussed mental health (for example, as part of a wellness campaign or other official communication)?' | [
"What",
"was",
"the",
"percentage",
"for",
"the",
"answer",
"of",
"\"",
"Yes",
"\"",
"was",
"given",
"to",
"the",
"question",
"\"",
"Has",
"your",
"employer",
"ever",
"formally",
"discussed",
"mental",
"health",
"(",
"for",
"example",
",",
"as",
"part",
... | [
{
"id": 3,
"type": "value",
"value": "Has your employer ever formally discussed mental health (for example, as part of a wellness campaign or other official communication)?"
},
{
"id": 2,
"type": "column",
"value": "questiontext"
},
{
"id": 4,
"type": "column",
"value": "... | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
15
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
17,
18,
19,
20,
21,
22,
23,
24,
25,
2... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",... |
15,667 | shop_membership | spider:train_spider.json:5402 | Show name, address road, and city for all branches sorted by open year. | SELECT name , address_road , city FROM branch ORDER BY open_year | [
"Show",
"name",
",",
"address",
"road",
",",
"and",
"city",
"for",
"all",
"branches",
"sorted",
"by",
"open",
"year",
"."
] | [
{
"id": 2,
"type": "column",
"value": "address_road"
},
{
"id": 4,
"type": "column",
"value": "open_year"
},
{
"id": 0,
"type": "table",
"value": "branch"
},
{
"id": 1,
"type": "column",
"value": "name"
},
{
"id": 3,
"type": "column",
"valu... | [
{
"entity_id": 0,
"token_idxs": [
10
]
},
{
"entity_id": 1,
"token_idxs": [
1
]
},
{
"entity_id": 2,
"token_idxs": [
3,
4
]
},
{
"entity_id": 3,
"token_idxs": [
7
]
},
{
"entity_id": 4,
"token_idxs": [
13,
... | [
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O"
] |
15,668 | financial | bird:dev.json:112 | For the female client who was born in 1976/1/29, which district did she opened her account? | SELECT T1.A2 FROM district AS T1 INNER JOIN client AS T2 ON T1.district_id = T2.district_id WHERE T2.birth_date = '1976-01-29' AND T2.gender = 'F' | [
"For",
"the",
"female",
"client",
"who",
"was",
"born",
"in",
"1976/1/29",
",",
"which",
"district",
"did",
"she",
"opened",
"her",
"account",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "district_id"
},
{
"id": 4,
"type": "column",
"value": "birth_date"
},
{
"id": 5,
"type": "value",
"value": "1976-01-29"
},
{
"id": 1,
"type": "table",
"value": "district"
},
{
"id": 2,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
11
]
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": [
12
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O"
] |
15,669 | movie_3 | bird:train.json:9207 | What is the description and film title of ID 996? | SELECT description, title FROM film_text WHERE film_id = 996 | [
"What",
"is",
"the",
"description",
"and",
"film",
"title",
"of",
"ID",
"996",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "description"
},
{
"id": 0,
"type": "table",
"value": "film_text"
},
{
"id": 3,
"type": "column",
"value": "film_id"
},
{
"id": 2,
"type": "column",
"value": "title"
},
{
"id": 4,
"type": "value",
"valu... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": [
5
]
},
{
"entity_id": 4,
"token_idxs": [
9
]
},
{
"entity_... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O"
] |
15,670 | card_games | bird:dev.json:395 | How many cards designed by UDON and available in mtgo print type has a starting maximum hand size of -1? | SELECT COUNT(id) FROM cards WHERE hAND = '-1' AND artist = 'UDON' AND Availability = 'mtgo' | [
"How",
"many",
"cards",
"designed",
"by",
"UDON",
"and",
"available",
"in",
"mtgo",
"print",
"type",
"has",
"a",
"starting",
"maximum",
"hand",
"size",
"of",
"-1",
"?"
] | [
{
"id": 6,
"type": "column",
"value": "availability"
},
{
"id": 4,
"type": "column",
"value": "artist"
},
{
"id": 0,
"type": "table",
"value": "cards"
},
{
"id": 2,
"type": "column",
"value": "hand"
},
{
"id": 5,
"type": "value",
"value": "... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
16
]
},
{
"entity_id": 3,
"token_idxs": [
19
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O"
] |
15,671 | genes | bird:train.json:2489 | For the genes that are located in the plasma membrane, please list their number of chromosomes. | SELECT T1.Chromosome FROM Genes AS T1 INNER JOIN Classification AS T2 ON T1.GeneID = T2.GeneID WHERE T2.Localization = 'plasma membrane' | [
"For",
"the",
"genes",
"that",
"are",
"located",
"in",
"the",
"plasma",
"membrane",
",",
"please",
"list",
"their",
"number",
"of",
"chromosomes",
"."
] | [
{
"id": 4,
"type": "value",
"value": "plasma membrane"
},
{
"id": 2,
"type": "table",
"value": "classification"
},
{
"id": 3,
"type": "column",
"value": "localization"
},
{
"id": 0,
"type": "column",
"value": "chromosome"
},
{
"id": 5,
"type": ... | [
{
"entity_id": 0,
"token_idxs": [
16
]
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
5,
6
]
},
{
"entity_id": 4,
"token_idxs": [
8,
9
]
... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
15,672 | medicine_enzyme_interaction | spider:train_spider.json:960 | How many medicines were not approved by the FDA? | SELECT count(*) FROM medicine WHERE FDA_approved = 'No' | [
"How",
"many",
"medicines",
"were",
"not",
"approved",
"by",
"the",
"FDA",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "fda_approved"
},
{
"id": 0,
"type": "table",
"value": "medicine"
},
{
"id": 2,
"type": "value",
"value": "No"
}
] | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
4
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"B-COLUMN",
"O",
"O",
"O",
"O"
] |
15,673 | works_cycles | bird:train.json:7390 | What are the names of the vendor with the second lowest minimum order quantity? | SELECT T2.Name FROM ProductVendor AS T1 INNER JOIN Vendor AS T2 ON T1.BusinessEntityID = T2.BusinessEntityID ORDER BY T1.MaxOrderQty ASC LIMIT 1, 1 | [
"What",
"are",
"the",
"names",
"of",
"the",
"vendor",
"with",
"the",
"second",
"lowest",
"minimum",
"order",
"quantity",
"?"
] | [
{
"id": 4,
"type": "column",
"value": "businessentityid"
},
{
"id": 1,
"type": "table",
"value": "productvendor"
},
{
"id": 3,
"type": "column",
"value": "maxorderqty"
},
{
"id": 2,
"type": "table",
"value": "vendor"
},
{
"id": 0,
"type": "colu... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": [
12,
13
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O"
] |
15,674 | professional_basketball | bird:train.json:2849 | Among players who were born after 1950, who had offence rebounds rates more than 30%? Please list their last names and first names. | SELECT DISTINCT T1.lastName, T1.firstName FROM players AS T1 INNER JOIN player_allstar AS T2 ON T1.playerID = T2.playerID WHERE T1.birthDate > 1950 AND CAST(T2.o_rebounds AS REAL) * 100 / T2.rebounds > 30 | [
"Among",
"players",
"who",
"were",
"born",
"after",
"1950",
",",
"who",
"had",
"offence",
"rebounds",
"rates",
"more",
"than",
"30",
"%",
"?",
"Please",
"list",
"their",
"last",
"names",
"and",
"first",
"names",
"."
] | [
{
"id": 3,
"type": "table",
"value": "player_allstar"
},
{
"id": 10,
"type": "column",
"value": "o_rebounds"
},
{
"id": 1,
"type": "column",
"value": "firstname"
},
{
"id": 5,
"type": "column",
"value": "birthdate"
},
{
"id": 0,
"type": "column... | [
{
"entity_id": 0,
"token_idxs": [
21,
22
]
},
{
"entity_id": 1,
"token_idxs": [
24,
25
]
},
{
"entity_id": 2,
"token_idxs": [
1
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
... | [
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O"
] |
15,675 | behavior_monitoring | spider:train_spider.json:3107 | Find the id and last name of the teacher that has the most detentions with detention type code "AFTER"? | SELECT T1.teacher_id , T2.last_name FROM Detention AS T1 JOIN Teachers AS T2 ON T1.teacher_id = T2.teacher_id WHERE T1.detention_type_code = "AFTER" GROUP BY T1.teacher_id ORDER BY count(*) DESC LIMIT 1 | [
"Find",
"the",
"i",
"d",
"and",
"last",
"name",
"of",
"the",
"teacher",
"that",
"has",
"the",
"most",
"detentions",
"with",
"detention",
"type",
"code",
"\"",
"AFTER",
"\"",
"?"
] | [
{
"id": 4,
"type": "column",
"value": "detention_type_code"
},
{
"id": 0,
"type": "column",
"value": "teacher_id"
},
{
"id": 1,
"type": "column",
"value": "last_name"
},
{
"id": 2,
"type": "table",
"value": "detention"
},
{
"id": 3,
"type": "ta... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
5,
6
]
},
{
"entity_id": 2,
"token_idxs": [
16
]
},
{
"entity_id": 3,
"token_idxs": [
9
]
},
{
"entity_id": 4,
"token_idxs": [
17,
18
]
... | [
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"O",
"O"
] |
15,676 | airline | bird:train.json:5881 | Which flight carrier operator flies from Atlantic City to Fort Lauderdale? | SELECT T2.Description FROM Airlines AS T1 INNER JOIN `Air Carriers` AS T2 ON T1.OP_CARRIER_AIRLINE_ID = T2.Code WHERE T1.ORIGIN = 'ACY' AND T1.DEST = 'FLL' GROUP BY T2.Description | [
"Which",
"flight",
"carrier",
"operator",
"flies",
"from",
"Atlantic",
"City",
"to",
"Fort",
"Lauderdale",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "op_carrier_airline_id"
},
{
"id": 2,
"type": "table",
"value": "Air Carriers"
},
{
"id": 0,
"type": "column",
"value": "description"
},
{
"id": 1,
"type": "table",
"value": "airlines"
},
{
"id": 5,
"type":... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
2
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"B-TABLE",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
15,677 | student_loan | bird:train.json:4452 | Which school has the highest number of disabled students? | SELECT T.school FROM ( SELECT T2.school, COUNT(T2.name) AS num FROM disabled AS T1 INNER JOIN enrolled AS T2 ON T1.name = T2.name GROUP BY T2.school ) T ORDER BY T.num DESC LIMIT 1 | [
"Which",
"school",
"has",
"the",
"highest",
"number",
"of",
"disabled",
"students",
"?"
] | [
{
"id": 2,
"type": "table",
"value": "disabled"
},
{
"id": 3,
"type": "table",
"value": "enrolled"
},
{
"id": 0,
"type": "column",
"value": "school"
},
{
"id": 4,
"type": "column",
"value": "name"
},
{
"id": 1,
"type": "column",
"value": "n... | [
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-TABLE",
"O",
"O"
] |
15,678 | shooting | bird:train.json:2479 | Of the black officers, how many of them investigated cases between the years 2010 and 2015? | SELECT COUNT(T1.case_number) FROM officers AS T1 INNER JOIN incidents AS T2 ON T2.case_number = T1.case_number WHERE T1.race = 'B' AND T2.date BETWEEN '2010-01-01' AND '2015-12-31' | [
"Of",
"the",
"black",
"officers",
",",
"how",
"many",
"of",
"them",
"investigated",
"cases",
"between",
"the",
"years",
"2010",
"and",
"2015",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "case_number"
},
{
"id": 6,
"type": "value",
"value": "2010-01-01"
},
{
"id": 7,
"type": "value",
"value": "2015-12-31"
},
{
"id": 1,
"type": "table",
"value": "incidents"
},
{
"id": 0,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": [
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",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
15,679 | european_football_2 | bird:dev.json:1029 | What are the speed in which attacks are put together of the top 4 teams with the highest build Up Play Speed? | SELECT t1.buildUpPlaySpeed FROM Team_Attributes AS t1 INNER JOIN Team AS t2 ON t1.team_api_id = t2.team_api_id ORDER BY t1.buildUpPlaySpeed ASC LIMIT 4 | [
"What",
"are",
"the",
"speed",
"in",
"which",
"attacks",
"are",
"put",
"together",
"of",
"the",
"top",
"4",
"teams",
"with",
"the",
"highest",
"build",
"Up",
"Play",
"Speed",
"?"
] | [
{
"id": 0,
"type": "column",
"value": "buildupplayspeed"
},
{
"id": 1,
"type": "table",
"value": "team_attributes"
},
{
"id": 3,
"type": "column",
"value": "team_api_id"
},
{
"id": 2,
"type": "table",
"value": "team"
}
] | [
{
"entity_id": 0,
"token_idxs": [
18,
19,
20,
21
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
14
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"e... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O"
] |
15,681 | entrepreneur | spider:train_spider.json:2283 | What are the dates of birth of entrepreneurs with investor "Simon Woodroffe" or "Peter Jones"? | SELECT T2.Date_of_Birth FROM entrepreneur AS T1 JOIN people AS T2 ON T1.People_ID = T2.People_ID WHERE T1.Investor = "Simon Woodroffe" OR T1.Investor = "Peter Jones" | [
"What",
"are",
"the",
"dates",
"of",
"birth",
"of",
"entrepreneurs",
"with",
"investor",
"\"",
"Simon",
"Woodroffe",
"\"",
"or",
"\"",
"Peter",
"Jones",
"\"",
"?"
] | [
{
"id": 5,
"type": "column",
"value": "Simon Woodroffe"
},
{
"id": 0,
"type": "column",
"value": "date_of_birth"
},
{
"id": 1,
"type": "table",
"value": "entrepreneur"
},
{
"id": 6,
"type": "column",
"value": "Peter Jones"
},
{
"id": 3,
"type":... | [
{
"entity_id": 0,
"token_idxs": [
3,
4,
5
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
9
]
},
{
"en... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O"
] |
15,683 | csu_1 | spider:train_spider.json:2334 | Find the name of the campuses that is in Northridge, Los Angeles or in San Francisco, San Francisco. | SELECT campus FROM campuses WHERE LOCATION = "Northridge" AND county = "Los Angeles" UNION SELECT campus FROM campuses WHERE LOCATION = "San Francisco" AND county = "San Francisco" | [
"Find",
"the",
"name",
"of",
"the",
"campuses",
"that",
"is",
"in",
"Northridge",
",",
"Los",
"Angeles",
"or",
"in",
"San",
"Francisco",
",",
"San",
"Francisco",
"."
] | [
{
"id": 6,
"type": "column",
"value": "San Francisco"
},
{
"id": 5,
"type": "column",
"value": "Los Angeles"
},
{
"id": 3,
"type": "column",
"value": "Northridge"
},
{
"id": 0,
"type": "table",
"value": "campuses"
},
{
"id": 2,
"type": "column"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
9
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O"
] |
15,684 | flight_1 | spider:train_spider.json:348 | What are the ids of all aircrafts that can cover a distance of more than 1000? | SELECT aid FROM Aircraft WHERE distance > 1000 | [
"What",
"are",
"the",
"ids",
"of",
"all",
"aircrafts",
"that",
"can",
"cover",
"a",
"distance",
"of",
"more",
"than",
"1000",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "aircraft"
},
{
"id": 2,
"type": "column",
"value": "distance"
},
{
"id": 3,
"type": "value",
"value": "1000"
},
{
"id": 1,
"type": "column",
"value": "aid"
}
] | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
11
]
},
{
"entity_id": 3,
"token_idxs": [
15
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entit... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
15,685 | california_schools | bird:dev.json:7 | What is the phone number of the school that has the highest number of test takers with an SAT score of over 1500? | SELECT T2.Phone FROM satscores AS T1 INNER JOIN schools AS T2 ON T1.cds = T2.CDSCode ORDER BY T1.NumGE1500 DESC LIMIT 1 | [
"What",
"is",
"the",
"phone",
"number",
"of",
"the",
"school",
"that",
"has",
"the",
"highest",
"number",
"of",
"test",
"takers",
"with",
"an",
"SAT",
"score",
"of",
"over",
"1500",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "satscores"
},
{
"id": 3,
"type": "column",
"value": "numge1500"
},
{
"id": 2,
"type": "table",
"value": "schools"
},
{
"id": 5,
"type": "column",
"value": "cdscode"
},
{
"id": 0,
"type": "column",
"valu... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
18
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": [
22
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entit... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O"
] |
15,686 | pilot_1 | bird:test.json:1166 | What are the different plane names of planes with an average pilot age of below 35, and how many pilots have flown each of them? | SELECT count(*) , plane_name FROM pilotskills GROUP BY plane_name HAVING avg(age) < 35 | [
"What",
"are",
"the",
"different",
"plane",
"names",
"of",
"planes",
"with",
"an",
"average",
"pilot",
"age",
"of",
"below",
"35",
",",
"and",
"how",
"many",
"pilots",
"have",
"flown",
"each",
"of",
"them",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "pilotskills"
},
{
"id": 1,
"type": "column",
"value": "plane_name"
},
{
"id": 3,
"type": "column",
"value": "age"
},
{
"id": 2,
"type": "value",
"value": "35"
}
] | [
{
"entity_id": 0,
"token_idxs": [
20
]
},
{
"entity_id": 1,
"token_idxs": [
4,
5
]
},
{
"entity_id": 2,
"token_idxs": [
15
]
},
{
"entity_id": 3,
"token_idxs": [
12
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O"
] |
15,687 | works_cycles | bird:train.json:7253 | What is the average age of the sales agents in the company by 12/31/2009? | SELECT AVG(2009 - STRFTIME('%Y', T2.BirthDate)) FROM Person AS T1 INNER JOIN Employee AS T2 ON T1.BusinessEntityID = T2.BusinessEntityID WHERE T1.PersonType = 'SP' | [
"What",
"is",
"the",
"average",
"age",
"of",
"the",
"sales",
"agents",
"in",
"the",
"company",
"by",
"12/31/2009",
"?"
] | [
{
"id": 4,
"type": "column",
"value": "businessentityid"
},
{
"id": 2,
"type": "column",
"value": "persontype"
},
{
"id": 7,
"type": "column",
"value": "birthdate"
},
{
"id": 1,
"type": "table",
"value": "employee"
},
{
"id": 0,
"type": "table"... | [
{
"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"
] |
15,688 | perpetrator | spider:train_spider.json:2322 | Show the date of the tallest perpetrator. | SELECT T2.Date FROM people AS T1 JOIN perpetrator AS T2 ON T1.People_ID = T2.People_ID ORDER BY T1.Height DESC LIMIT 1 | [
"Show",
"the",
"date",
"of",
"the",
"tallest",
"perpetrator",
"."
] | [
{
"id": 2,
"type": "table",
"value": "perpetrator"
},
{
"id": 4,
"type": "column",
"value": "people_id"
},
{
"id": 1,
"type": "table",
"value": "people"
},
{
"id": 3,
"type": "column",
"value": "height"
},
{
"id": 0,
"type": "column",
"valu... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
15,689 | video_games | bird:train.json:3506 | In games that can be played on Wii, what is the percentage of games released in 2007? | SELECT CAST(COUNT(CASE WHEN T2.release_year = 2007 THEN T3.game_id ELSE NULL END) AS REAL) * 100 / COUNT(T3.game_id) FROM platform AS T1 INNER JOIN game_platform AS T2 ON T1.id = T2.platform_id INNER JOIN game_publisher AS T3 ON T2.game_publisher_id = T3.id WHERE T1.platform_name = 'Wii' | [
"In",
"games",
"that",
"can",
"be",
"played",
"on",
"Wii",
",",
"what",
"is",
"the",
"percentage",
"of",
"games",
"released",
"in",
"2007",
"?"
] | [
{
"id": 5,
"type": "column",
"value": "game_publisher_id"
},
{
"id": 0,
"type": "table",
"value": "game_publisher"
},
{
"id": 1,
"type": "column",
"value": "platform_name"
},
{
"id": 4,
"type": "table",
"value": "game_platform"
},
{
"id": 10,
"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"B-VALUE",
"O"
] |
15,690 | restaurant | bird:train.json:1679 | What type of food is there in the restaurants on Adeline Street in Berkeley city? | SELECT T1.food_type FROM generalinfo AS T1 INNER JOIN location AS T2 ON T1.id_restaurant = T2.id_restaurant WHERE T2.street_name = 'adeline st' AND T2.city = 'berkeley' | [
"What",
"type",
"of",
"food",
"is",
"there",
"in",
"the",
"restaurants",
"on",
"Adeline",
"Street",
"in",
"Berkeley",
"city",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "id_restaurant"
},
{
"id": 1,
"type": "table",
"value": "generalinfo"
},
{
"id": 4,
"type": "column",
"value": "street_name"
},
{
"id": 5,
"type": "value",
"value": "adeline st"
},
{
"id": 0,
"type": "colum... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"O",
"B-VALUE",
"B-COLUMN",
"O"
] |
15,693 | cre_Doc_Tracking_DB | spider:train_spider.json:4163 | What are all the calendar dates and day Numbers? | SELECT calendar_date , day_Number FROM Ref_calendar | [
"What",
"are",
"all",
"the",
"calendar",
"dates",
"and",
"day",
"Numbers",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "calendar_date"
},
{
"id": 0,
"type": "table",
"value": "ref_calendar"
},
{
"id": 2,
"type": "column",
"value": "day_number"
}
] | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
7,
8
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id":... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-COLUMN",
"I-COLUMN",
"O"
] |
15,694 | game_1 | spider:train_spider.json:6002 | How many different students are involved in sports? | SELECT count(DISTINCT StuID) FROM Sportsinfo | [
"How",
"many",
"different",
"students",
"are",
"involved",
"in",
"sports",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "sportsinfo"
},
{
"id": 1,
"type": "column",
"value": "stuid"
}
] | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
15,695 | toxicology | bird:dev.json:234 | How many bonds which involved atom 12 does molecule TR009 have? | SELECT COUNT(T2.bond_id) FROM bond AS T1 INNER JOIN connected AS T2 ON T1.bond_id = T2.bond_id WHERE T1.molecule_id = 'TR009' AND T2.atom_id = T1.molecule_id || '_1' AND T2.atom_id2 = T1.molecule_id || '_2' | [
"How",
"many",
"bonds",
"which",
"involved",
"atom",
"12",
"does",
"molecule",
"TR009",
"have",
"?"
] | [
{
"id": 3,
"type": "column",
"value": "molecule_id"
},
{
"id": 1,
"type": "table",
"value": "connected"
},
{
"id": 6,
"type": "column",
"value": "atom_id2"
},
{
"id": 2,
"type": "column",
"value": "bond_id"
},
{
"id": 5,
"type": "column",
"... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": [
9
]
},
{
"entity_id": 5,
"... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"B-COLUMN",
"B-VALUE",
"O",
"O"
] |
15,696 | county_public_safety | spider:train_spider.json:2538 | What are the minimum and maximum crime rate of counties? | SELECT min(Crime_rate) , max(Crime_rate) FROM county_public_safety | [
"What",
"are",
"the",
"minimum",
"and",
"maximum",
"crime",
"rate",
"of",
"counties",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "county_public_safety"
},
{
"id": 1,
"type": "column",
"value": "crime_rate"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
6,
7
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O"
] |
15,697 | video_games | bird:train.json:3343 | How many games did Electronic Arts publish? | SELECT COUNT(DISTINCT T2.game_id) FROM publisher AS T1 INNER JOIN game_publisher AS T2 ON T1.id = T2.publisher_id WHERE T1.publisher_name = 'Electronic Arts' | [
"How",
"many",
"games",
"did",
"Electronic",
"Arts",
"publish",
"?"
] | [
{
"id": 3,
"type": "value",
"value": "Electronic Arts"
},
{
"id": 1,
"type": "table",
"value": "game_publisher"
},
{
"id": 2,
"type": "column",
"value": "publisher_name"
},
{
"id": 6,
"type": "column",
"value": "publisher_id"
},
{
"id": 0,
"typ... | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
4,
5
]
},
{
"entity_id": 4,
"token_idxs": [
2
]
},
{
"entity_id":... | [
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"B-VALUE",
"I-VALUE",
"B-TABLE",
"O"
] |
15,698 | formula_1 | spider:train_spider.json:2222 | What is the maximum fastest lap speed in the Monaco Grand Prix in 2008? | SELECT max(T2.fastestlapspeed) FROM races AS T1 JOIN results AS T2 ON T1.raceid = T2.raceid WHERE T1.year = 2008 AND T1.name = "Monaco Grand Prix" | [
"What",
"is",
"the",
"maximum",
"fastest",
"lap",
"speed",
"in",
"the",
"Monaco",
"Grand",
"Prix",
"in",
"2008",
"?"
] | [
{
"id": 7,
"type": "column",
"value": "Monaco Grand Prix"
},
{
"id": 2,
"type": "column",
"value": "fastestlapspeed"
},
{
"id": 1,
"type": "table",
"value": "results"
},
{
"id": 3,
"type": "column",
"value": "raceid"
},
{
"id": 0,
"type": "tabl... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
4,
5,
6
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_id... | [
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O",
"B-VALUE",
"O"
] |
15,699 | manufactory_1 | spider:train_spider.json:5282 | Find the total revenue created by the companies whose headquarter is located at Austin. | SELECT sum(revenue) FROM manufacturers WHERE headquarter = 'Austin' | [
"Find",
"the",
"total",
"revenue",
"created",
"by",
"the",
"companies",
"whose",
"headquarter",
"is",
"located",
"at",
"Austin",
"."
] | [
{
"id": 0,
"type": "table",
"value": "manufacturers"
},
{
"id": 1,
"type": "column",
"value": "headquarter"
},
{
"id": 3,
"type": "column",
"value": "revenue"
},
{
"id": 2,
"type": "value",
"value": "Austin"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
9
]
},
{
"entity_id": 2,
"token_idxs": [
13
]
},
{
"entity_id": 3,
"token_idxs": [
3
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
15,700 | small_bank_1 | spider:train_spider.json:1813 | What is the sum of checking and savings balances for all customers, ordered by the total balance? | SELECT T1.balance + T2.balance FROM checking AS T1 JOIN savings AS T2 ON T1.custid = T2.custid ORDER BY T1.balance + T2.balance | [
"What",
"is",
"the",
"sum",
"of",
"checking",
"and",
"savings",
"balances",
"for",
"all",
"customers",
",",
"ordered",
"by",
"the",
"total",
"balance",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "checking"
},
{
"id": 1,
"type": "table",
"value": "savings"
},
{
"id": 2,
"type": "column",
"value": "balance"
},
{
"id": 3,
"type": "column",
"value": "custid"
}
] | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": [
17
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
15,701 | game_1 | spider:train_spider.json:5981 | How many video games have type Massively multiplayer online game? | SELECT count(*) FROM Video_games WHERE gtype = "Massively multiplayer online game" | [
"How",
"many",
"video",
"games",
"have",
"type",
"Massively",
"multiplayer",
"online",
"game",
"?"
] | [
{
"id": 2,
"type": "column",
"value": "Massively multiplayer online game"
},
{
"id": 0,
"type": "table",
"value": "video_games"
},
{
"id": 1,
"type": "column",
"value": "gtype"
}
] | [
{
"entity_id": 0,
"token_idxs": [
2,
3
]
},
{
"entity_id": 1,
"token_idxs": [
5
]
},
{
"entity_id": 2,
"token_idxs": [
6,
7,
8,
9
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [... | [
"O",
"O",
"B-TABLE",
"I-TABLE",
"O",
"B-COLUMN",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"I-COLUMN",
"O"
] |
15,702 | beer_factory | bird:train.json:5237 | In which city is the brewery AJ Stephans Beverages located? | SELECT City FROM rootbeerbrand WHERE BreweryName = 'AJ Stephans Beverages' | [
"In",
"which",
"city",
"is",
"the",
"brewery",
"AJ",
"Stephans",
"Beverages",
"located",
"?"
] | [
{
"id": 3,
"type": "value",
"value": "AJ Stephans Beverages"
},
{
"id": 0,
"type": "table",
"value": "rootbeerbrand"
},
{
"id": 2,
"type": "column",
"value": "breweryname"
},
{
"id": 1,
"type": "column",
"value": "city"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
2
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": [
6,
7,
8
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"en... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O"
] |
15,703 | hockey | bird:train.json:7805 | List down player ID of players who have passed away. | SELECT DISTINCT playerID FROM Master WHERE deathYear IS NOT NULL AND playerID IS NOT NULL | [
"List",
"down",
"player",
"ID",
"of",
"players",
"who",
"have",
"passed",
"away",
"."
] | [
{
"id": 2,
"type": "column",
"value": "deathyear"
},
{
"id": 1,
"type": "column",
"value": "playerid"
},
{
"id": 0,
"type": "table",
"value": "master"
}
] | [
{
"entity_id": 0,
"token_idxs": []
},
{
"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,
"token_idxs": []
... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
15,704 | movie_1 | spider:train_spider.json:2445 | What is the id of the reviewer whose name includes the word "Mike"? | SELECT rID FROM Reviewer WHERE name LIKE "%Mike%" | [
"What",
"is",
"the",
"i",
"d",
"of",
"the",
"reviewer",
"whose",
"name",
"includes",
"the",
"word",
"\"",
"Mike",
"\"",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "reviewer"
},
{
"id": 3,
"type": "column",
"value": "%Mike%"
},
{
"id": 2,
"type": "column",
"value": "name"
},
{
"id": 1,
"type": "column",
"value": "rid"
}
] | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
3,
4
]
},
{
"entity_id": 2,
"token_idxs": [
9
]
},
{
"entity_id": 3,
"token_idxs": [
14
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O"
] |
15,706 | talkingdata | bird:train.json:1081 | Among the users who use a Galaxy Note 2, how many of them are female? | SELECT COUNT(T1.device_id) FROM phone_brand_device_model2 AS T1 INNER JOIN gender_age AS T2 ON T2.device_id = T1.device_id WHERE T2.gender = 'F' AND T1.device_model = 'Galaxy Note 2' | [
"Among",
"the",
"users",
"who",
"use",
"a",
"Galaxy",
"Note",
"2",
",",
"how",
"many",
"of",
"them",
"are",
"female",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "phone_brand_device_model2"
},
{
"id": 6,
"type": "value",
"value": "Galaxy Note 2"
},
{
"id": 5,
"type": "column",
"value": "device_model"
},
{
"id": 1,
"type": "table",
"value": "gender_age"
},
{
"id": 2,
... | [
{
"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": [
12
]
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O"
] |
15,707 | e_commerce | bird:test.json:86 | What are the product sizes of the products whose name has the substring 'Dell'? | SELECT product_size FROM Products WHERE product_name LIKE '%Dell%' | [
"What",
"are",
"the",
"product",
"sizes",
"of",
"the",
"products",
"whose",
"name",
"has",
"the",
"substring",
"'",
"Dell",
"'",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "product_size"
},
{
"id": 2,
"type": "column",
"value": "product_name"
},
{
"id": 0,
"type": "table",
"value": "products"
},
{
"id": 3,
"type": "value",
"value": "%Dell%"
}
] | [
{
"entity_id": 0,
"token_idxs": [
7
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
3
]
},
{
"entity_id": 3,
"token_idxs": [
14
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity... | [
"O",
"O",
"O",
"B-COLUMN",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O"
] |
15,708 | movie_3 | bird:train.json:9204 | Calculate the average payment amount per customer. | SELECT AVG(amount) FROM payment GROUP BY customer_id | [
"Calculate",
"the",
"average",
"payment",
"amount",
"per",
"customer",
"."
] | [
{
"id": 1,
"type": "column",
"value": "customer_id"
},
{
"id": 0,
"type": "table",
"value": "payment"
},
{
"id": 2,
"type": "column",
"value": "amount"
}
] | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": [
4
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-COLUMN",
"O"
] |
15,710 | talkingdata | bird:train.json:1150 | How many categories in total do the app users who were not active when event no.2 happened belong to? | SELECT COUNT(*) FROM ( SELECT COUNT(DISTINCT T1.category) AS result FROM label_categories AS T1 INNER JOIN app_labels AS T2 ON T1.label_id = T2.label_id INNER JOIN app_events AS T3 ON T2.app_id = T3.app_id WHERE T3.event_id = 2 AND T3.is_active = 0 GROUP BY T1.category ) T | [
"How",
"many",
"categories",
"in",
"total",
"do",
"the",
"app",
"users",
"who",
"were",
"not",
"active",
"when",
"event",
"no.2",
"happened",
"belong",
"to",
"?"
] | [
{
"id": 2,
"type": "table",
"value": "label_categories"
},
{
"id": 1,
"type": "table",
"value": "app_events"
},
{
"id": 3,
"type": "table",
"value": "app_labels"
},
{
"id": 7,
"type": "column",
"value": "is_active"
},
{
"id": 0,
"type": "column... | [
{
"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": [
7
]
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O"
] |
15,711 | art_1 | bird:test.json:1260 | What are the first and last name of the artist who had a sculpture work whose title has the word “female” in it? | SELECT T1.lname , T1.fname FROM artists AS T1 JOIN sculptures AS T2 ON T1.artistID = T2.sculptorID WHERE T2.title LIKE "%female%" | [
"What",
"are",
"the",
"first",
"and",
"last",
"name",
"of",
"the",
"artist",
"who",
"had",
"a",
"sculpture",
"work",
"whose",
"title",
"has",
"the",
"word",
"“",
"female",
"”",
"in",
"it",
"?"
] | [
{
"id": 3,
"type": "table",
"value": "sculptures"
},
{
"id": 7,
"type": "column",
"value": "sculptorid"
},
{
"id": 5,
"type": "column",
"value": "%female%"
},
{
"id": 6,
"type": "column",
"value": "artistid"
},
{
"id": 2,
"type": "table",
"... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
6
]
},
{
"entity_id": 2,
"token_idxs": [
9
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
16
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O"
] |
15,712 | books | bird:train.json:5949 | How many orders did Antonia Poltun return? | SELECT COUNT(*) FROM order_status AS T1 INNER JOIN order_history AS T2 ON T1.status_id = T2.status_id INNER JOIN cust_order AS T3 ON T3.order_id = T2.order_id INNER JOIN customer AS T4 ON T4.customer_id = T3.customer_id WHERE T1.status_value = 'Returned' AND T4.first_name = 'Antonia' AND T4.last_name = 'Poltun' | [
"How",
"many",
"orders",
"did",
"Antonia",
"Poltun",
"return",
"?"
] | [
{
"id": 10,
"type": "table",
"value": "order_history"
},
{
"id": 3,
"type": "column",
"value": "status_value"
},
{
"id": 9,
"type": "table",
"value": "order_status"
},
{
"id": 2,
"type": "column",
"value": "customer_id"
},
{
"id": 1,
"type": "t... | [
{
"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": [
6
]
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"B-VALUE",
"B-VALUE",
"B-VALUE",
"O"
] |
15,713 | mondial_geo | bird:train.json:8308 | Among the countries whose government type is republic, how many of them shares a border that's longer than 200? | SELECT COUNT(DISTINCT T1.Name) FROM country AS T1 INNER JOIN politics AS T2 ON T1.Code = T2.Country INNER JOIN borders AS T3 ON T3.Country1 = T2.Country WHERE T2.Government = 'republic' AND T3.Length > 200 | [
"Among",
"the",
"countries",
"whose",
"government",
"type",
"is",
"republic",
",",
"how",
"many",
"of",
"them",
"shares",
"a",
"border",
"that",
"'s",
"longer",
"than",
"200",
"?"
] | [
{
"id": 6,
"type": "column",
"value": "government"
},
{
"id": 3,
"type": "table",
"value": "politics"
},
{
"id": 4,
"type": "column",
"value": "country1"
},
{
"id": 7,
"type": "value",
"value": "republic"
},
{
"id": 0,
"type": "table",
"val... | [
{
"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": [
2
]
},
{
... | [
"O",
"O",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"B-VALUE",
"O"
] |
15,714 | college_1 | spider:train_spider.json:3300 | What is the name of the department with the student that has the lowest GPA? | SELECT T2.dept_name FROM student AS T1 JOIN department AS T2 ON T1.dept_code = T2.dept_code ORDER BY stu_gpa LIMIT 1 | [
"What",
"is",
"the",
"name",
"of",
"the",
"department",
"with",
"the",
"student",
"that",
"has",
"the",
"lowest",
"GPA",
"?"
] | [
{
"id": 2,
"type": "table",
"value": "department"
},
{
"id": 0,
"type": "column",
"value": "dept_name"
},
{
"id": 4,
"type": "column",
"value": "dept_code"
},
{
"id": 1,
"type": "table",
"value": "student"
},
{
"id": 3,
"type": "column",
"v... | [
{
"entity_id": 0,
"token_idxs": [
6
]
},
{
"entity_id": 1,
"token_idxs": [
9
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
14
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
15,715 | address | bird:train.json:5177 | What are the zip code for the Senate house? | SELECT T2.zip_code FROM congress AS T1 INNER JOIN zip_congress AS T2 ON T1.cognress_rep_id = T2.district WHERE T1.House = 'House of Repsentatives' GROUP BY T2.zip_code | [
"What",
"are",
"the",
"zip",
"code",
"for",
"the",
"Senate",
"house",
"?"
] | [
{
"id": 4,
"type": "value",
"value": "House of Repsentatives"
},
{
"id": 5,
"type": "column",
"value": "cognress_rep_id"
},
{
"id": 2,
"type": "table",
"value": "zip_congress"
},
{
"id": 0,
"type": "column",
"value": "zip_code"
},
{
"id": 1,
"t... | [
{
"entity_id": 0,
"token_idxs": [
3,
4
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": [
5,
6,
7
]
},
... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"B-COLUMN",
"O"
] |
15,716 | assets_maintenance | spider:train_spider.json:3136 | What are all the fault descriptions and the fault status of all the faults recoreded in the logs? | SELECT T1.fault_description , T2.fault_status FROM Fault_Log AS T1 JOIN Fault_Log_Parts AS T2 ON T1.fault_log_entry_id = T2.fault_log_entry_id | [
"What",
"are",
"all",
"the",
"fault",
"descriptions",
"and",
"the",
"fault",
"status",
"of",
"all",
"the",
"faults",
"recoreded",
"in",
"the",
"logs",
"?"
] | [
{
"id": 4,
"type": "column",
"value": "fault_log_entry_id"
},
{
"id": 0,
"type": "column",
"value": "fault_description"
},
{
"id": 3,
"type": "table",
"value": "fault_log_parts"
},
{
"id": 1,
"type": "column",
"value": "fault_status"
},
{
"id": 2,
... | [
{
"entity_id": 0,
"token_idxs": [
5
]
},
{
"entity_id": 1,
"token_idxs": [
8,
9
]
},
{
"entity_id": 2,
"token_idxs": [
4
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id":... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
] |
15,717 | ship_1 | spider:train_spider.json:6252 | List all ship names in the order of built year and class. | SELECT name FROM ship ORDER BY built_year , CLASS | [
"List",
"all",
"ship",
"names",
"in",
"the",
"order",
"of",
"built",
"year",
"and",
"class",
"."
] | [
{
"id": 2,
"type": "column",
"value": "built_year"
},
{
"id": 3,
"type": "column",
"value": "class"
},
{
"id": 0,
"type": "table",
"value": "ship"
},
{
"id": 1,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
8,
9
]
},
{
"entity_id": 3,
"token_idxs": [
11
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
... | [
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-COLUMN",
"O"
] |
15,718 | mondial_geo | bird:train.json:8311 | How many mountains are there on the African Continent? | SELECT COUNT(T3.Name) FROM country AS T1 INNER JOIN encompasses AS T2 ON T1.Code = T2.Country INNER JOIN continent AS T3 ON T3.Name = T2.Continent INNER JOIN province AS T4 ON T4.Country = T1.Code INNER JOIN geo_mountain AS T5 ON T5.Province = T4.Name WHERE T3.Name = 'European' | [
"How",
"many",
"mountains",
"are",
"there",
"on",
"the",
"African",
"Continent",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "geo_mountain"
},
{
"id": 9,
"type": "table",
"value": "encompasses"
},
{
"id": 5,
"type": "table",
"value": "continent"
},
{
"id": 10,
"type": "column",
"value": "continent"
},
{
"id": 2,
"type": "value",
... | [
{
"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",
"B-COLUMN",
"O"
] |
15,719 | planet_1 | bird:test.json:1885 | Who sent more than one packages? List the client's name. | SELECT T2.Name , count(*) FROM PACKAGE AS T1 JOIN Client AS T2 ON T1.Sender = T2.AccountNumber GROUP BY T1.Sender HAVING count(*) > 1; | [
"Who",
"sent",
"more",
"than",
"one",
"packages",
"?",
"List",
"the",
"client",
"'s",
"name",
"."
] | [
{
"id": 5,
"type": "column",
"value": "accountnumber"
},
{
"id": 2,
"type": "table",
"value": "package"
},
{
"id": 0,
"type": "column",
"value": "sender"
},
{
"id": 3,
"type": "table",
"value": "client"
},
{
"id": 1,
"type": "column",
"valu... | [
{
"entity_id": 0,
"token_idxs": [
1
]
},
{
"entity_id": 1,
"token_idxs": [
11
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": [
9
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity... | [
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O"
] |
15,720 | advertising_agencies | bird:test.json:2090 | What are the client id and details for the client with the most invoices? | SELECT T1.client_id , T2.client_details FROM Invoices AS T1 JOIN Clients AS T2 ON T1.client_id = T2.client_id GROUP BY T1.client_id ORDER BY count(*) DESC LIMIT 1 | [
"What",
"are",
"the",
"client",
"i",
"d",
"and",
"details",
"for",
"the",
"client",
"with",
"the",
"most",
"invoices",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "client_details"
},
{
"id": 0,
"type": "column",
"value": "client_id"
},
{
"id": 2,
"type": "table",
"value": "invoices"
},
{
"id": 3,
"type": "table",
"value": "clients"
}
] | [
{
"entity_id": 0,
"token_idxs": [
3,
4,
5
]
},
{
"entity_id": 1,
"token_idxs": [
6,
7
]
},
{
"entity_id": 2,
"token_idxs": [
14
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_id... | [
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"I-COLUMN",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-TABLE",
"O"
] |
15,721 | customers_and_addresses | spider:train_spider.json:6057 | How many customers are there? | SELECT count(*) FROM customers | [
"How",
"many",
"customers",
"are",
"there",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "customers"
}
] | [
{
"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"
] |
15,722 | student_loan | bird:train.json:4465 | How many students are enlisted in the Navy organization? | SELECT COUNT(name) FROM enlist WHERE organ = 'navy' | [
"How",
"many",
"students",
"are",
"enlisted",
"in",
"the",
"Navy",
"organization",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "enlist"
},
{
"id": 1,
"type": "column",
"value": "organ"
},
{
"id": 2,
"type": "value",
"value": "navy"
},
{
"id": 3,
"type": "column",
"value": "name"
}
] | [
{
"entity_id": 0,
"token_idxs": [
4
]
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"entity_id": 2,
"token_idxs": [
7
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O"
] |
15,723 | retails | bird:train.json:6799 | Please state the segment, the name, the address, and the phone number of customer number 3. | SELECT c_mktsegment, c_name, c_address, c_phone FROM customer WHERE c_custkey = 3 | [
"Please",
"state",
"the",
"segment",
",",
"the",
"name",
",",
"the",
"address",
",",
"and",
"the",
"phone",
"number",
"of",
"customer",
"number",
"3",
"."
] | [
{
"id": 1,
"type": "column",
"value": "c_mktsegment"
},
{
"id": 3,
"type": "column",
"value": "c_address"
},
{
"id": 5,
"type": "column",
"value": "c_custkey"
},
{
"id": 0,
"type": "table",
"value": "customer"
},
{
"id": 4,
"type": "column",
... | [
{
"entity_id": 0,
"token_idxs": [
16
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": [
9
]
},
{
"entity_id": 4,
"token_idxs": [
13
]
},
... | [
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"O"
] |
15,724 | university | bird:train.json:7987 | How many universities have at least 80,000 students in the year 2011? | SELECT COUNT(*) FROM university_year WHERE num_students > 80000 AND year = 2011 | [
"How",
"many",
"universities",
"have",
"at",
"least",
"80,000",
"students",
"in",
"the",
"year",
"2011",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "university_year"
},
{
"id": 1,
"type": "column",
"value": "num_students"
},
{
"id": 2,
"type": "value",
"value": "80000"
},
{
"id": 3,
"type": "column",
"value": "year"
},
{
"id": 4,
"type": "value",
"v... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
7
]
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": [
10
]
},
{
"entity_id": 4,
"token_idxs": [
11
]
},
... | [
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"O",
"O",
"B-COLUMN",
"B-VALUE",
"O"
] |
15,725 | works_cycles | bird:train.json:7079 | Among the active employees with over 10 hours of sick leave, what is the percentage of the employees with over 20 vacation hours? | SELECT CAST(SUM(CASE WHEN T2.VacationHours > 20 THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(T1.BusinessEntityID) FROM EmployeePayHistory AS T1 INNER JOIN Employee AS T2 ON T1.BusinessEntityID = T2.BusinessEntityID WHERE T2.CurrentFlag = 1 AND T2.SickLeaveHours > 10 | [
"Among",
"the",
"active",
"employees",
"with",
"over",
"10",
"hours",
"of",
"sick",
"leave",
",",
"what",
"is",
"the",
"percentage",
"of",
"the",
"employees",
"with",
"over",
"20",
"vacation",
"hours",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "employeepayhistory"
},
{
"id": 2,
"type": "column",
"value": "businessentityid"
},
{
"id": 5,
"type": "column",
"value": "sickleavehours"
},
{
"id": 9,
"type": "column",
"value": "vacationhours"
},
{
"id": 3,
... | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
18
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
6
]
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-VALUE",
"B-COLUMN",
"I-COLUMN",
"O"
] |
15,726 | ice_hockey_draft | bird:train.json:6966 | Among the Italian players, who has the shortest height? | SELECT T2.PlayerName FROM height_info AS T1 INNER JOIN PlayerInfo AS T2 ON T1.height_id = T2.height WHERE T2.nation = 'Italy' ORDER BY T1.height_in_cm ASC LIMIT 1 | [
"Among",
"the",
"Italian",
"players",
",",
"who",
"has",
"the",
"shortest",
"height",
"?"
] | [
{
"id": 5,
"type": "column",
"value": "height_in_cm"
},
{
"id": 1,
"type": "table",
"value": "height_info"
},
{
"id": 0,
"type": "column",
"value": "playername"
},
{
"id": 2,
"type": "table",
"value": "playerinfo"
},
{
"id": 6,
"type": "column"... | [
{
"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": [
2
]
},
{
"entity_id": 5,
"token_idxs": ... | [
"O",
"O",
"B-VALUE",
"B-TABLE",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
15,727 | cre_Doc_Tracking_DB | spider:train_spider.json:4240 | Show the ids of the employees who don't authorize destruction for any document. | SELECT employee_id FROM Employees EXCEPT SELECT Destruction_Authorised_by_Employee_ID FROM Documents_to_be_destroyed | [
"Show",
"the",
"ids",
"of",
"the",
"employees",
"who",
"do",
"n't",
"authorize",
"destruction",
"for",
"any",
"document",
"."
] | [
{
"id": 3,
"type": "column",
"value": "destruction_authorised_by_employee_id"
},
{
"id": 1,
"type": "table",
"value": "documents_to_be_destroyed"
},
{
"id": 2,
"type": "column",
"value": "employee_id"
},
{
"id": 0,
"type": "table",
"value": "employees"
}... | [
{
"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",
"O",
"O"
] |
15,728 | citeseer | bird:train.json:4142 | For all words cited in paper ID 315017, state the other paper ID and class label which also cited those words. | SELECT T1.class_label, T2.word_cited_id FROM paper AS T1 INNER JOIN content AS T2 ON T1.paper_id = T2.paper_id WHERE T1.paper_id = 315017 | [
"For",
"all",
"words",
"cited",
"in",
"paper",
"ID",
"315017",
",",
"state",
"the",
"other",
"paper",
"ID",
"and",
"class",
"label",
"which",
"also",
"cited",
"those",
"words",
"."
] | [
{
"id": 1,
"type": "column",
"value": "word_cited_id"
},
{
"id": 0,
"type": "column",
"value": "class_label"
},
{
"id": 4,
"type": "column",
"value": "paper_id"
},
{
"id": 3,
"type": "table",
"value": "content"
},
{
"id": 5,
"type": "value",
... | [
{
"entity_id": 0,
"token_idxs": [
15,
16
]
},
{
"entity_id": 1,
"token_idxs": [
2,
3
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
6
]
... | [
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"B-TABLE",
"B-COLUMN",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O"
] |
15,729 | cs_semester | bird:train.json:876 | Which professor is Oliy Spratling working with? Give the full name. | SELECT T1.first_name, T1.last_name 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 WHERE T3.f_name = 'Oliy' AND T3.l_name = 'Spratling' | [
"Which",
"professor",
"is",
"Oliy",
"Spratling",
"working",
"with",
"?",
"Give",
"the",
"full",
"name",
"."
] | [
{
"id": 0,
"type": "column",
"value": "first_name"
},
{
"id": 5,
"type": "column",
"value": "student_id"
},
{
"id": 1,
"type": "column",
"value": "last_name"
},
{
"id": 9,
"type": "value",
"value": "Spratling"
},
{
"id": 2,
"type": "table",
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": [
1
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"token_idxs": []
},
{
... | [
"O",
"B-TABLE",
"O",
"B-VALUE",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
15,730 | shop_membership | spider:train_spider.json:5426 | List the branch name and city without any registered members. | SELECT name , city FROM branch WHERE branch_id NOT IN (SELECT branch_id FROM membership_register_branch) | [
"List",
"the",
"branch",
"name",
"and",
"city",
"without",
"any",
"registered",
"members",
"."
] | [
{
"id": 4,
"type": "table",
"value": "membership_register_branch"
},
{
"id": 3,
"type": "column",
"value": "branch_id"
},
{
"id": 0,
"type": "table",
"value": "branch"
},
{
"id": 1,
"type": "column",
"value": "name"
},
{
"id": 2,
"type": "colum... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": [
3
]
},
{
"entity_id": 2,
"token_idxs": [
5
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
"... | [
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O"
] |
15,731 | soccer_2 | spider:train_spider.json:5017 | Which colleges does each player with a name that starts with the letter D who tried out go to? | SELECT T1.cName FROM tryout AS T1 JOIN player AS T2 ON T1.pID = T2.pID WHERE T2.pName LIKE 'D%' | [
"Which",
"colleges",
"does",
"each",
"player",
"with",
"a",
"name",
"that",
"starts",
"with",
"the",
"letter",
"D",
" ",
"who",
"tried",
"out",
"go",
"to",
"?"
] | [
{
"id": 1,
"type": "table",
"value": "tryout"
},
{
"id": 2,
"type": "table",
"value": "player"
},
{
"id": 0,
"type": "column",
"value": "cname"
},
{
"id": 3,
"type": "column",
"value": "pname"
},
{
"id": 5,
"type": "column",
"value": "pid"
... | [
{
"entity_id": 0,
"token_idxs": []
},
{
"entity_id": 1,
"token_idxs": [
17
]
},
{
"entity_id": 2,
"token_idxs": [
4
]
},
{
"entity_id": 3,
"token_idxs": [
7
]
},
{
"entity_id": 4,
"token_idxs": [
13
]
},
{
"entit... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"B-TABLE",
"O",
"O",
"O"
] |
15,732 | shakespeare | bird:train.json:3051 | Among paragraphs with paragraph number between 1900 to 1950, list the texts said by a character described as a sea captain, friend to Sebatian. | SELECT T1.description FROM characters AS T1 INNER JOIN paragraphs AS T2 ON T1.id = T2.character_id WHERE T2.PlainText = 'a sea captain, friend to Sebastian' AND T2.ParagraphNum BETWEEN 1500 AND 1950 | [
"Among",
"paragraphs",
"with",
"paragraph",
"number",
"between",
"1900",
"to",
"1950",
",",
"list",
"the",
"texts",
"said",
"by",
"a",
"character",
"described",
"as",
"a",
"sea",
"captain",
",",
"friend",
"to",
"Sebatian",
"."
] | [
{
"id": 6,
"type": "value",
"value": "a sea captain, friend to Sebastian"
},
{
"id": 4,
"type": "column",
"value": "character_id"
},
{
"id": 7,
"type": "column",
"value": "paragraphnum"
},
{
"id": 0,
"type": "column",
"value": "description"
},
{
"i... | [
{
"entity_id": 0,
"token_idxs": [
17
]
},
{
"entity_id": 1,
"token_idxs": [
16
]
},
{
"entity_id": 2,
"token_idxs": [
1
]
},
{
"entity_id": 3,
"token_idxs": [
13
]
},
{
"entity_id": 4,
"token_idxs": []
},
{
"enti... | [
"O",
"B-TABLE",
"O",
"B-COLUMN",
"O",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O"
] |
15,733 | bakery_1 | bird:test.json:1546 | What is the receipt number with the latest date, and what is that date? | SELECT ReceiptNumber , date FROM receipts WHERE date = (SELECT date FROM receipts ORDER BY date DESC LIMIT 1) | [
"What",
"is",
"the",
"receipt",
"number",
"with",
"the",
"latest",
"date",
",",
"and",
"what",
"is",
"that",
"date",
"?"
] | [
{
"id": 1,
"type": "column",
"value": "receiptnumber"
},
{
"id": 0,
"type": "table",
"value": "receipts"
},
{
"id": 2,
"type": "column",
"value": "date"
}
] | [
{
"entity_id": 0,
"token_idxs": [
3
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
14
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-COLUMN",
"O"
] |
15,734 | baseball_1 | spider:train_spider.json:3637 | How many players enter hall of fame each year? | SELECT yearid , count(*) FROM hall_of_fame GROUP BY yearid; | [
"How",
"many",
"players",
"enter",
"hall",
"of",
"fame",
"each",
"year",
"?"
] | [
{
"id": 0,
"type": "table",
"value": "hall_of_fame"
},
{
"id": 1,
"type": "column",
"value": "yearid"
}
] | [
{
"entity_id": 0,
"token_idxs": [
4,
5,
6
]
},
{
"entity_id": 1,
"token_idxs": [
8
]
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": []
},
{
"entity_id": 5,
... | [
"O",
"O",
"O",
"O",
"B-TABLE",
"I-TABLE",
"I-TABLE",
"O",
"B-COLUMN",
"O"
] |
15,735 | tracking_grants_for_research | spider:train_spider.json:4366 | For grants with both documents described as 'Regular' and documents described as 'Initial Application', list its start date. | SELECT T1.grant_start_date FROM Grants AS T1 JOIN Documents AS T2 ON T1.grant_id = T2.grant_id JOIN Document_Types AS T3 ON T2.document_type_code = T3.document_type_code WHERE T3.document_description = 'Regular' INTERSECT SELECT T1.grant_start_date FROM Grants AS T1 JOIN Documents AS T2 ON T1.grant_id = T2.grant_id JOIN Document_Types AS T3 ON T2.document_type_code = T3.document_type_code WHERE T3.document_description = 'Initial Application' | [
"For",
"grants",
"with",
"both",
"documents",
"described",
"as",
"'",
"Regular",
"'",
"and",
"documents",
"described",
"as",
"'",
"Initial",
"Application",
"'",
",",
"list",
"its",
"start",
"date",
"."
] | [
{
"id": 2,
"type": "column",
"value": "document_description"
},
{
"id": 4,
"type": "value",
"value": "Initial Application"
},
{
"id": 7,
"type": "column",
"value": "document_type_code"
},
{
"id": 0,
"type": "column",
"value": "grant_start_date"
},
{
... | [
{
"entity_id": 0,
"token_idxs": [
21,
22
]
},
{
"entity_id": 1,
"token_idxs": [
4
]
},
{
"entity_id": 2,
"token_idxs": [
12
]
},
{
"entity_id": 3,
"token_idxs": [
8
]
},
{
"entity_id": 4,
"token_idxs": [
15,
... | [
"O",
"B-TABLE",
"O",
"O",
"B-TABLE",
"O",
"O",
"O",
"B-VALUE",
"O",
"O",
"B-TABLE",
"B-COLUMN",
"O",
"O",
"B-VALUE",
"I-VALUE",
"O",
"O",
"O",
"O",
"B-COLUMN",
"I-COLUMN",
"O"
] |
15,736 | genes | bird:train.json:2491 | Among the genes with nucleic acid metabolism defects, how many of them can be found in the vacuole? | SELECT COUNT(T1.GeneID) FROM Genes AS T1 INNER JOIN Classification AS T2 ON T1.GeneID = T2.GeneID WHERE T2.Localization = 'vacuole' AND T1.Phenotype = 'Nucleic acid metabolism defects' | [
"Among",
"the",
"genes",
"with",
"nucleic",
"acid",
"metabolism",
"defects",
",",
"how",
"many",
"of",
"them",
"can",
"be",
"found",
"in",
"the",
"vacuole",
"?"
] | [
{
"id": 6,
"type": "value",
"value": "Nucleic acid metabolism defects"
},
{
"id": 1,
"type": "table",
"value": "classification"
},
{
"id": 3,
"type": "column",
"value": "localization"
},
{
"id": 5,
"type": "column",
"value": "phenotype"
},
{
"id": ... | [
{
"entity_id": 0,
"token_idxs": [
2
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": []
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
18
]
},
{
"entity_id": 5,
"token_idxs":... | [
"O",
"O",
"B-TABLE",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"O"
] |
15,737 | tracking_grants_for_research | spider:train_spider.json:4376 | How many project staff worked as leaders or started working before '1989-04-24 23:51:54'? | SELECT count(*) FROM Project_Staff WHERE role_code = 'leader' OR date_from < '1989-04-24 23:51:54' | [
"How",
"many",
"project",
"staff",
"worked",
"as",
"leaders",
"or",
"started",
"working",
"before",
"'",
"1989",
"-",
"04",
"-",
"24",
"23:51:54",
"'",
"?"
] | [
{
"id": 4,
"type": "value",
"value": "1989-04-24 23:51:54"
},
{
"id": 0,
"type": "table",
"value": "project_staff"
},
{
"id": 1,
"type": "column",
"value": "role_code"
},
{
"id": 3,
"type": "column",
"value": "date_from"
},
{
"id": 2,
"type": "... | [
{
"entity_id": 0,
"token_idxs": [
2,
3
]
},
{
"entity_id": 1,
"token_idxs": []
},
{
"entity_id": 2,
"token_idxs": [
6
]
},
{
"entity_id": 3,
"token_idxs": []
},
{
"entity_id": 4,
"token_idxs": [
12,
13,
14,
15,... | [
"O",
"O",
"B-TABLE",
"I-TABLE",
"O",
"O",
"B-VALUE",
"O",
"O",
"O",
"O",
"O",
"B-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"I-VALUE",
"O",
"O"
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.