QIAN-ME/bird_schema_data huggingface dataset:
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 law_episode
【Schema】
# Table: Credit
[
(episode_id:TEXT, Primary Key, Examples: [tt0629146, tt0629149, tt0629153]),
(person_id:TEXT, Primary Key, Examples: [nm0000973, nm0001583, nm0001832]),
(category:TEXT, Examples: [Additional Crew]),
(role:TEXT, Examples: [technical advisor]),
(credited:TEXT, Examples: [true, false])
]
# Table: Person
[
(person_id:TEXT, Primary Key, Examples: [nm0000210, nm0000973, nm0001346]),
(name:TEXT, Examples: [Julia Roberts, Rene Balcer, Billy Fox]),
(birthdate:DATE, Examples: [1967-10-28]),
(birth_name:TEXT, Examples: [Julia Fiona Roberts]),
(birth_place:TEXT, Examples: [Smyrna, Montreal, Harrisburg]),
(birth_region:TEXT, Examples: [Georgia, Quebec, Pennsylvania]),
(birth_country:TEXT, Examples: [USA, Canada, England]),
(height_meters:REAL, Examples: [1.73, 1.88, 1.93]),
(nickname:TEXT, Examples: [Jules, Gus, Ed])
]
# Table: Award
[
(award_id:INTEGER, Primary Key, Examples: [258, 259, 260]),
(organization:TEXT),
(year:INTEGER, Examples: [1999, 2000]),
(award_category:TEXT, Examples: [Monitor, Primetime Emmy, Silver Gavel Award]),
(award:TEXT),
(series:TEXT, Examples: [Law and Order]),
(episode_id:TEXT, Examples: [tt0629149, tt0629398, tt0629248]),
(person_id:TEXT, Examples: [nm0937725, nm0792309, nm0049569]),
(role:TEXT, Examples: [Katrina Ludlow, production mixer, re-recording mixer]),
(result:TEXT, Examples: [Winner, Nominee])
]
# Table: Vote
[
(episode_id:TEXT, Examples: [tt0629204, tt0629228, tt0629170]),
(stars:INTEGER, Examples: [10, 9, 8]),
(votes:INTEGER, Examples: [36, 39, 64]),
(percent:REAL, Examples: [17.7, 19.2, 31.5])
]
# Table: Episode
[
(episode_id:TEXT, Primary Key, Examples: [tt0629146, tt0629149, tt0629153]),
(series:TEXT, Examples: [Law and Order]),
(season:INTEGER, Examples: [9]),
(episode:INTEGER, Examples: [1, 2, 3]),
(number_in_series:INTEGER, Examples: [182, 183, 184]),
(title:TEXT, Examples: [Cherished, DWB, Bait]),
(summary:TEXT),
(air_date:DATE, Examples: [1998-09-23]),
(episode_image:TEXT),
(rating:REAL, Examples: [7.9, 7.6, 8.2]),
(votes:INTEGER, Examples: [203, 177, 159])
]
# Table: Keyword
[
(episode_id:TEXT, Primary Key, Examples: [tt0629239, tt0629397, tt0629420]),
(keyword:TEXT, Primary Key, Examples: [nun, priest, police officer])
]
【Foreign keys】
Credit.episode_id=Episode.episode_id
Credit.person_id=Person.person_id
Award.episode_id=Episode.episode_id
Award.person_id=Person.person_id
Vote.episode_id=Episode.episode_id
Keyword.episode_id=Episode.episode_id

    【Question】
    Which episode has the highest total number of viewer votes? episode refers to title; the highest total number of viewer votes refers to max(sum(votes))

    【Evidence】
    episode refers to title; the highest total number of viewer votes refers to max(sum(votes))

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT T1.title FROM Episode AS T1 INNER JOIN Vote AS T2 ON T1.episode_id = T2.episode_id GROUP BY T1.title ORDER BY SUM(T1.votes) DESC LIMIT 1
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 law_episode
【Schema】
# Table: Credit
[
(episode_id:TEXT, Primary Key, Examples: [tt0629146, tt0629149, tt0629153]),
(person_id:TEXT, Primary Key, Examples: [nm0000973, nm0001583, nm0001832]),
(category:TEXT, Examples: [Additional Crew]),
(role:TEXT, Examples: [technical advisor]),
(credited:TEXT, Examples: [true, false])
]
# Table: Person
[
(person_id:TEXT, Primary Key, Examples: [nm0000210, nm0000973, nm0001346]),
(name:TEXT, Examples: [Julia Roberts, Rene Balcer, Billy Fox]),
(birthdate:DATE, Examples: [1967-10-28]),
(birth_name:TEXT, Examples: [Julia Fiona Roberts]),
(birth_place:TEXT, Examples: [Smyrna, Montreal, Harrisburg]),
(birth_region:TEXT, Examples: [Georgia, Quebec, Pennsylvania]),
(birth_country:TEXT, Examples: [USA, Canada, England]),
(height_meters:REAL, Examples: [1.73, 1.88, 1.93]),
(nickname:TEXT, Examples: [Jules, Gus, Ed])
]
# Table: Award
[
(award_id:INTEGER, Primary Key, Examples: [258, 259, 260]),
(organization:TEXT),
(year:INTEGER, Examples: [1999, 2000]),
(award_category:TEXT, Examples: [Monitor, Primetime Emmy, Silver Gavel Award]),
(award:TEXT),
(series:TEXT, Examples: [Law and Order]),
(episode_id:TEXT, Examples: [tt0629149, tt0629398, tt0629248]),
(person_id:TEXT, Examples: [nm0937725, nm0792309, nm0049569]),
(role:TEXT, Examples: [Katrina Ludlow, production mixer, re-recording mixer]),
(result:TEXT, Examples: [Winner, Nominee])
]
# Table: Vote
[
(episode_id:TEXT, Examples: [tt0629204, tt0629228, tt0629170]),
(stars:INTEGER, Examples: [10, 9, 8]),
(votes:INTEGER, Examples: [36, 39, 64]),
(percent:REAL, Examples: [17.7, 19.2, 31.5])
]
# Table: Episode
[
(episode_id:TEXT, Primary Key, Examples: [tt0629146, tt0629149, tt0629153]),
(series:TEXT, Examples: [Law and Order]),
(season:INTEGER, Examples: [9]),
(episode:INTEGER, Examples: [1, 2, 3]),
(number_in_series:INTEGER, Examples: [182, 183, 184]),
(title:TEXT, Examples: [Cherished, DWB, Bait]),
(summary:TEXT),
(air_date:DATE, Examples: [1998-09-23]),
(episode_image:TEXT),
(rating:REAL, Examples: [7.9, 7.6, 8.2]),
(votes:INTEGER, Examples: [203, 177, 159])
]
# Table: Keyword
[
(episode_id:TEXT, Primary Key, Examples: [tt0629239, tt0629397, tt0629420]),
(keyword:TEXT, Primary Key, Examples: [nun, priest, police officer])
]
【Foreign keys】
Credit.episode_id=Episode.episode_id
Credit.person_id=Person.person_id
Award.episode_id=Episode.episode_id
Award.person_id=Person.person_id
Vote.episode_id=Episode.episode_id
Keyword.episode_id=Episode.episode_id

    【Question】
    What are the keywords of the episode "Shield"? the episode "Shield" refers to title = 'Shield'

    【Evidence】
    the episode "Shield" refers to title = 'Shield'

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT T2.keyword FROM Episode AS T1 INNER JOIN Keyword AS T2 ON T1.episode_id = T2.episode_id WHERE T1.title = 'Shield'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 law_episode
【Schema】
# Table: Credit
[
(episode_id:TEXT, Primary Key, Examples: [tt0629146, tt0629149, tt0629153]),
(person_id:TEXT, Primary Key, Examples: [nm0000973, nm0001583, nm0001832]),
(category:TEXT, Examples: [Additional Crew]),
(role:TEXT, Examples: [technical advisor]),
(credited:TEXT, Examples: [true, false])
]
# Table: Person
[
(person_id:TEXT, Primary Key, Examples: [nm0000210, nm0000973, nm0001346]),
(name:TEXT, Examples: [Julia Roberts, Rene Balcer, Billy Fox]),
(birthdate:DATE, Examples: [1967-10-28]),
(birth_name:TEXT, Examples: [Julia Fiona Roberts]),
(birth_place:TEXT, Examples: [Smyrna, Montreal, Harrisburg]),
(birth_region:TEXT, Examples: [Georgia, Quebec, Pennsylvania]),
(birth_country:TEXT, Examples: [USA, Canada, England]),
(height_meters:REAL, Examples: [1.73, 1.88, 1.93]),
(nickname:TEXT, Examples: [Jules, Gus, Ed])
]
# Table: Award
[
(award_id:INTEGER, Primary Key, Examples: [258, 259, 260]),
(organization:TEXT),
(year:INTEGER, Examples: [1999, 2000]),
(award_category:TEXT, Examples: [Monitor, Primetime Emmy, Silver Gavel Award]),
(award:TEXT),
(series:TEXT, Examples: [Law and Order]),
(episode_id:TEXT, Examples: [tt0629149, tt0629398, tt0629248]),
(person_id:TEXT, Examples: [nm0937725, nm0792309, nm0049569]),
(role:TEXT, Examples: [Katrina Ludlow, production mixer, re-recording mixer]),
(result:TEXT, Examples: [Winner, Nominee])
]
# Table: Vote
[
(episode_id:TEXT, Examples: [tt0629204, tt0629228, tt0629170]),
(stars:INTEGER, Examples: [10, 9, 8]),
(votes:INTEGER, Examples: [36, 39, 64]),
(percent:REAL, Examples: [17.7, 19.2, 31.5])
]
# Table: Episode
[
(episode_id:TEXT, Primary Key, Examples: [tt0629146, tt0629149, tt0629153]),
(series:TEXT, Examples: [Law and Order]),
(season:INTEGER, Examples: [9]),
(episode:INTEGER, Examples: [1, 2, 3]),
(number_in_series:INTEGER, Examples: [182, 183, 184]),
(title:TEXT, Examples: [Cherished, DWB, Bait]),
(summary:TEXT),
(air_date:DATE, Examples: [1998-09-23]),
(episode_image:TEXT),
(rating:REAL, Examples: [7.9, 7.6, 8.2]),
(votes:INTEGER, Examples: [203, 177, 159])
]
# Table: Keyword
[
(episode_id:TEXT, Primary Key, Examples: [tt0629239, tt0629397, tt0629420]),
(keyword:TEXT, Primary Key, Examples: [nun, priest, police officer])
]
【Foreign keys】
Credit.episode_id=Episode.episode_id
Credit.person_id=Person.person_id
Award.episode_id=Episode.episode_id
Award.person_id=Person.person_id
Vote.episode_id=Episode.episode_id
Keyword.episode_id=Episode.episode_id

    【Question】
    Which episode has the two keywords "nun" and "priest"? episode refers to title; the two keywords "nun" and "priest" refers to keyword = 'nun' or keyword = 'priest';

    【Evidence】
    episode refers to title; the two keywords "nun" and "priest" refers to keyword = 'nun' or keyword = 'priest';

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT T1.title FROM Episode AS T1 INNER JOIN Keyword AS T2 ON T1.episode_id = T2.episode_id WHERE T2.keyword IN ('nun', 'priest')
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 law_episode
【Schema】
# Table: Credit
[
(episode_id:TEXT, Primary Key, Examples: [tt0629146, tt0629149, tt0629153]),
(person_id:TEXT, Primary Key, Examples: [nm0000973, nm0001583, nm0001832]),
(category:TEXT, Examples: [Additional Crew]),
(role:TEXT, Examples: [technical advisor]),
(credited:TEXT, Examples: [true, false])
]
# Table: Person
[
(person_id:TEXT, Primary Key, Examples: [nm0000210, nm0000973, nm0001346]),
(name:TEXT, Examples: [Julia Roberts, Rene Balcer, Billy Fox]),
(birthdate:DATE, Examples: [1967-10-28]),
(birth_name:TEXT, Examples: [Julia Fiona Roberts]),
(birth_place:TEXT, Examples: [Smyrna, Montreal, Harrisburg]),
(birth_region:TEXT, Examples: [Georgia, Quebec, Pennsylvania]),
(birth_country:TEXT, Examples: [USA, Canada, England]),
(height_meters:REAL, Examples: [1.73, 1.88, 1.93]),
(nickname:TEXT, Examples: [Jules, Gus, Ed])
]
# Table: Award
[
(award_id:INTEGER, Primary Key, Examples: [258, 259, 260]),
(organization:TEXT),
(year:INTEGER, Examples: [1999, 2000]),
(award_category:TEXT, Examples: [Monitor, Primetime Emmy, Silver Gavel Award]),
(award:TEXT),
(series:TEXT, Examples: [Law and Order]),
(episode_id:TEXT, Examples: [tt0629149, tt0629398, tt0629248]),
(person_id:TEXT, Examples: [nm0937725, nm0792309, nm0049569]),
(role:TEXT, Examples: [Katrina Ludlow, production mixer, re-recording mixer]),
(result:TEXT, Examples: [Winner, Nominee])
]
# Table: Vote
[
(episode_id:TEXT, Examples: [tt0629204, tt0629228, tt0629170]),
(stars:INTEGER, Examples: [10, 9, 8]),
(votes:INTEGER, Examples: [36, 39, 64]),
(percent:REAL, Examples: [17.7, 19.2, 31.5])
]
# Table: Episode
[
(episode_id:TEXT, Primary Key, Examples: [tt0629146, tt0629149, tt0629153]),
(series:TEXT, Examples: [Law and Order]),
(season:INTEGER, Examples: [9]),
(episode:INTEGER, Examples: [1, 2, 3]),
(number_in_series:INTEGER, Examples: [182, 183, 184]),
(title:TEXT, Examples: [Cherished, DWB, Bait]),
(summary:TEXT),
(air_date:DATE, Examples: [1998-09-23]),
(episode_image:TEXT),
(rating:REAL, Examples: [7.9, 7.6, 8.2]),
(votes:INTEGER, Examples: [203, 177, 159])
]
# Table: Keyword
[
(episode_id:TEXT, Primary Key, Examples: [tt0629239, tt0629397, tt0629420]),
(keyword:TEXT, Primary Key, Examples: [nun, priest, police officer])
]
【Foreign keys】
Credit.episode_id=Episode.episode_id
Credit.person_id=Person.person_id
Award.episode_id=Episode.episode_id
Award.person_id=Person.person_id
Vote.episode_id=Episode.episode_id
Keyword.episode_id=Episode.episode_id

    【Question】
    Please list any three episodes that were most enjoyed by the viewers. episode refers to title; most enjoyed by the viewers refers to stars = 10

    【Evidence】
    episode refers to title; most enjoyed by the viewers refers to stars = 10

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT T1.title FROM Episode AS T1 INNER JOIN Vote AS T2 ON T1.episode_id = T2.episode_id WHERE T2.stars = 10 LIMIT 3
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 restaurant
【Schema】
# Table: generalinfo
[
(id_restaurant:INTEGER, Primary Key, Examples: [1, 2, 3]),
(label:TEXT, Examples: [sparky's diner, kabul afghan cuisine, helmand restaurant]),
(food_type:TEXT, Examples: [24 hour diner, afghani, african]),
(city:TEXT, Examples: [san francisco, san carlos, sunnyvale]),
(review:REAL, Examples: [2.3, 3.8, 4.0])
]
# Table: geographic
[
(city:TEXT, Primary Key, Examples: [alameda, alamo, albany]),
(county:TEXT, Examples: [alameda county, contra costa county, santa clara county]),
(region:TEXT, Examples: [bay area, napa valley, unknown])
]
# Table: location
[
(id_restaurant:INTEGER, Primary Key, Examples: [1, 2, 3]),
(street_num:INTEGER, Examples: [242, 135, 430]),
(street_name:TEXT, Examples: [church st, el camino real, broadway]),
(city:TEXT, Examples: [san francisco, san carlos, sunnyvale])
]
【Foreign keys】
generalinfo.city=geographic.city
location.city=geographic.city
location.id_restaurant=generalinfo.id_restaurant

    【Question】
    How many restaurants have not obtained a minimum of 3 in their reviews? have not obtained a minimum of 3 in review refers to review < 3

    【Evidence】
    have not obtained a minimum of 3 in review refers to review < 3

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT COUNT(id_restaurant) FROM generalinfo WHERE review < 3
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 restaurant
【Schema】
# Table: generalinfo
[
(id_restaurant:INTEGER, Primary Key, Examples: [1, 2, 3]),
(label:TEXT, Examples: [sparky's diner, kabul afghan cuisine, helmand restaurant]),
(food_type:TEXT, Examples: [24 hour diner, afghani, african]),
(city:TEXT, Examples: [san francisco, san carlos, sunnyvale]),
(review:REAL, Examples: [2.3, 3.8, 4.0])
]
# Table: geographic
[
(city:TEXT, Primary Key, Examples: [alameda, alamo, albany]),
(county:TEXT, Examples: [alameda county, contra costa county, santa clara county]),
(region:TEXT, Examples: [bay area, napa valley, unknown])
]
# Table: location
[
(id_restaurant:INTEGER, Primary Key, Examples: [1, 2, 3]),
(street_num:INTEGER, Examples: [242, 135, 430]),
(street_name:TEXT, Examples: [church st, el camino real, broadway]),
(city:TEXT, Examples: [san francisco, san carlos, sunnyvale])
]
【Foreign keys】
generalinfo.city=geographic.city
location.city=geographic.city
location.id_restaurant=generalinfo.id_restaurant

    【Question】
    What types of food are served at the 4 top-reviewed restaurants? top-reviewed refers to review = 4; type of food refers to food_type

    【Evidence】
    top-reviewed refers to review = 4; type of food refers to food_type



    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT food_type FROM generalinfo WHERE review = ( SELECT MAX(review) FROM generalinfo ) LIMIT 4
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 restaurant
【Schema】
# Table: generalinfo
[
(id_restaurant:INTEGER, Primary Key, Examples: [1, 2, 3]),
(label:TEXT, Examples: [sparky's diner, kabul afghan cuisine, helmand restaurant]),
(food_type:TEXT, Examples: [24 hour diner, afghani, african]),
(city:TEXT, Examples: [san francisco, san carlos, sunnyvale]),
(review:REAL, Examples: [2.3, 3.8, 4.0])
]
# Table: geographic
[
(city:TEXT, Primary Key, Examples: [alameda, alamo, albany]),
(county:TEXT, Examples: [alameda county, contra costa county, santa clara county]),
(region:TEXT, Examples: [bay area, napa valley, unknown])
]
# Table: location
[
(id_restaurant:INTEGER, Primary Key, Examples: [1, 2, 3]),
(street_num:INTEGER, Examples: [242, 135, 430]),
(street_name:TEXT, Examples: [church st, el camino real, broadway]),
(city:TEXT, Examples: [san francisco, san carlos, sunnyvale])
]
【Foreign keys】
generalinfo.city=geographic.city
location.city=geographic.city
location.id_restaurant=generalinfo.id_restaurant

    【Question】
    How many restaurants in the city of Richmond serve Mediterranean food? Mediterranean food refers to food_type =   'mediterranean'

    【Evidence】
    Mediterranean food refers to food_type =   'mediterranean'

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT COUNT(id_restaurant) FROM generalinfo WHERE food_type = 'mediterranean' AND city = 'richmond'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 restaurant
【Schema】
# Table: generalinfo
[
(id_restaurant:INTEGER, Primary Key, Examples: [1, 2, 3]),
(label:TEXT, Examples: [sparky's diner, kabul afghan cuisine, helmand restaurant]),
(food_type:TEXT, Examples: [24 hour diner, afghani, african]),
(city:TEXT, Examples: [san francisco, san carlos, sunnyvale]),
(review:REAL, Examples: [2.3, 3.8, 4.0])
]
# Table: geographic
[
(city:TEXT, Primary Key, Examples: [alameda, alamo, albany]),
(county:TEXT, Examples: [alameda county, contra costa county, santa clara county]),
(region:TEXT, Examples: [bay area, napa valley, unknown])
]
# Table: location
[
(id_restaurant:INTEGER, Primary Key, Examples: [1, 2, 3]),
(street_num:INTEGER, Examples: [242, 135, 430]),
(street_name:TEXT, Examples: [church st, el camino real, broadway]),
(city:TEXT, Examples: [san francisco, san carlos, sunnyvale])
]
【Foreign keys】
generalinfo.city=geographic.city
location.city=geographic.city
location.id_restaurant=generalinfo.id_restaurant

    【Question】
    List all the cities in Sonoma County.

    【Evidence】
    

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT city FROM geographic WHERE county = 'sonoma county'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 restaurant
【Schema】
# Table: generalinfo
[
(id_restaurant:INTEGER, Primary Key, Examples: [1, 2, 3]),
(label:TEXT, Examples: [sparky's diner, kabul afghan cuisine, helmand restaurant]),
(food_type:TEXT, Examples: [24 hour diner, afghani, african]),
(city:TEXT, Examples: [san francisco, san carlos, sunnyvale]),
(review:REAL, Examples: [2.3, 3.8, 4.0])
]
# Table: geographic
[
(city:TEXT, Primary Key, Examples: [alameda, alamo, albany]),
(county:TEXT, Examples: [alameda county, contra costa county, santa clara county]),
(region:TEXT, Examples: [bay area, napa valley, unknown])
]
# Table: location
[
(id_restaurant:INTEGER, Primary Key, Examples: [1, 2, 3]),
(street_num:INTEGER, Examples: [242, 135, 430]),
(street_name:TEXT, Examples: [church st, el camino real, broadway]),
(city:TEXT, Examples: [san francisco, san carlos, sunnyvale])
]
【Foreign keys】
generalinfo.city=geographic.city
location.city=geographic.city
location.id_restaurant=generalinfo.id_restaurant

    【Question】
    What counties are not in the Bay Area Region? not in the Bay Area region refers to region ! = 'bay area'

    【Evidence】
    not in the Bay Area region refers to region ! = 'bay area'

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT DISTINCT county FROM geographic WHERE region != 'bay area'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 restaurant
【Schema】
# Table: generalinfo
[
(id_restaurant:INTEGER, Primary Key, Examples: [1, 2, 3]),
(label:TEXT, Examples: [sparky's diner, kabul afghan cuisine, helmand restaurant]),
(food_type:TEXT, Examples: [24 hour diner, afghani, african]),
(city:TEXT, Examples: [san francisco, san carlos, sunnyvale]),
(review:REAL, Examples: [2.3, 3.8, 4.0])
]
# Table: geographic
[
(city:TEXT, Primary Key, Examples: [alameda, alamo, albany]),
(county:TEXT, Examples: [alameda county, contra costa county, santa clara county]),
(region:TEXT, Examples: [bay area, napa valley, unknown])
]
# Table: location
[
(id_restaurant:INTEGER, Primary Key, Examples: [1, 2, 3]),
(street_num:INTEGER, Examples: [242, 135, 430]),
(street_name:TEXT, Examples: [church st, el camino real, broadway]),
(city:TEXT, Examples: [san francisco, san carlos, sunnyvale])
]
【Foreign keys】
generalinfo.city=geographic.city
location.city=geographic.city
location.id_restaurant=generalinfo.id_restaurant

    【Question】
    List all cities in the Northern California Region.

    【Evidence】
    

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT city FROM geographic WHERE region = 'northern california'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 restaurant
【Schema】
# Table: generalinfo
[
(id_restaurant:INTEGER, Primary Key, Examples: [1, 2, 3]),
(label:TEXT, Examples: [sparky's diner, kabul afghan cuisine, helmand restaurant]),
(food_type:TEXT, Examples: [24 hour diner, afghani, african]),
(city:TEXT, Examples: [san francisco, san carlos, sunnyvale]),
(review:REAL, Examples: [2.3, 3.8, 4.0])
]
# Table: geographic
[
(city:TEXT, Primary Key, Examples: [alameda, alamo, albany]),
(county:TEXT, Examples: [alameda county, contra costa county, santa clara county]),
(region:TEXT, Examples: [bay area, napa valley, unknown])
]
# Table: location
[
(id_restaurant:INTEGER, Primary Key, Examples: [1, 2, 3]),
(street_num:INTEGER, Examples: [242, 135, 430]),
(street_name:TEXT, Examples: [church st, el camino real, broadway]),
(city:TEXT, Examples: [san francisco, san carlos, sunnyvale])
]
【Foreign keys】
generalinfo.city=geographic.city
location.city=geographic.city
location.id_restaurant=generalinfo.id_restaurant

    【Question】
    List by its ID number all restaurants on 11th Street in Oakland. 11th Street refers to street_name = '11th street'; Oakland refers to city = 'oakland'; ID number of restaurant refers to id_restaurant

    【Evidence】
    11th Street refers to street_name = '11th street'; Oakland refers to city = 'oakland'; ID number of restaurant refers to id_restaurant



    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT id_restaurant FROM location WHERE city = 'oakland' AND street_name = '11th street'