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】
    What are the titles of the top 3 episodes that received no less than 30 votes in its 10-star rating? no less than 30 votes refers to votes > = 30; 10-star rating refers to stars = 10

    【Evidence】
    no less than 30 votes refers to votes > = 30; 10-star rating 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 T2.title FROM Vote AS T1 INNER JOIN Episode AS T2 ON T2.episode_id = T1.episode_id WHERE T1.votes >= 30 AND T1.stars = 10 ORDER BY T1.votes DESC LIMIT 3
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】
    How many people did not enjoy the finale episode? did not enjoy refers to stars = 1; the finale episode refers to episode = 24

    【Evidence】
    did not enjoy refers to stars = 1; the finale episode refers to episode = 24

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

SQL: SELECT COUNT(T1.episode_id) FROM Episode AS T1 INNER JOIN Vote AS T2 ON T1.episode_id = T2.episode_id WHERE T1.episode = 24 AND T2.stars = 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】
    List the names of all the cast members in the series. cast member refers to category = 'Cast'

    【Evidence】
    cast member refers to category = 'Cast'

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

SQL: SELECT T2.name FROM Credit AS T1 INNER JOIN Person AS T2 ON T2.person_id = T1.person_id WHERE T1.category = 'Cast'
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 episodes of the Law & Order have been nominated for the Primetime Emmy Awards? episode refers to award; the Primetime Emmy Awards refers to award_category like 'Primetime Emmy'

    【Evidence】
    episode refers to award; the Primetime Emmy Awards refers to award_category like 'Primetime Emmy'

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

SQL: SELECT DISTINCT episode_id FROM Award WHERE award_category = 'Primetime Emmy'
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】
    How many episodes have not won any Law & Order series awards? have not won any award refers to Result = 'Nominee'

    【Evidence】
    have not won any award refers to Result = 'Nominee'

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

SQL: SELECT COUNT(award_id) FROM Award WHERE Result = 'Nominee'
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 roles have not been credited at the end of the episodes? have not been credited refers to credited = ''

    【Evidence】
    have not been credited refers to credited = ''

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

SQL: SELECT DISTINCT role FROM Credit WHERE credited = 'false'
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 is the title of the 3 worst rated episodes? worst rated refers to min(rating)

    【Evidence】
    worst rated refers to min(rating)

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

SQL: SELECT title FROM Episode ORDER BY rating LIMIT 3
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 is the full place of birth of Rene Chenevert Balcer? full place of birth refers to birth_place, birth_region; Rene Chenevert Balcer refers to birth_name = 'Rene Chenevert Balcer'

    【Evidence】
    full place of birth refers to birth_place, birth_region; Rene Chenevert Balcer refers to birth_name = 'Rene Chenevert Balcer'

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

SQL: SELECT birth_place, birth_region FROM Person WHERE birth_name = 'Rene Chenevert Balcer'