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】
    List the titles and air dates of episodes that were produced by Billy Fox. produced refers to role = 'producer'

    【Evidence】
    produced refers to role = 'producer'

    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, T1.air_date FROM Episode AS T1 INNER JOIN Credit AS T2 ON T1.episode_id = T2.episode_id INNER JOIN Person AS T3 ON T3.person_id = T2.person_id WHERE T2.category = 'Produced by' AND T2.role = 'producer' AND T3.name = 'Billy Fox'
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 was the role of Jason Kuschner in episode 9?

    【Evidence】
    

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

SQL: SELECT T1.role FROM Credit AS T1 INNER JOIN Person AS T2 ON T2.person_id = T1.person_id INNER JOIN Episode AS T3 ON T1.episode_id = T3.episode_id WHERE T3.episode = 9 AND T2.name = 'Jason Kuschner'
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 down the titles of the top 3 episodes, from highest to lowest, in terms of their weighted stars. weighted stars = divide(sum(stars, percent), 100)

    【Evidence】
    weighted stars = divide(sum(stars, percent), 100)

    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.stars BETWEEN 1 AND 10 GROUP BY T2.title ORDER BY CAST(SUM(T1.stars * T1.percent) AS REAL) / 100 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 from Canada are nominated for an award? from Canada refers to birth_country = Canada; nominated refers to award is NOT NULL

    【Evidence】
    from Canada refers to birth_country = Canada; nominated refers to award is NOT NULL

    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.person_id) FROM Person AS T1 INNER JOIN Award AS T2 ON T1.person_id = T2.person_id WHERE T1.birth_country = 'Canada'
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 are credited to Jerry Orbach?

    【Evidence】
    

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

SQL: SELECT COUNT(T2.person_id) FROM Credit AS T1 INNER JOIN Person AS T2 ON T2.person_id = T1.person_id WHERE T2.name = 'Jerry Orbach'
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 out all the credit names for episode 9. credit name refers to name

    【Evidence】
    credit name refers to name

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

SQL: SELECT T3.name FROM Episode AS T1 INNER JOIN Credit AS T2 ON T1.episode_id = T2.episode_id INNER JOIN Person AS T3 ON T3.person_id = T2.person_id WHERE T1.episode = 9
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 out all award titles nominated for episode 20. award title refers to title; nominated refers to result = 'Winner' or result = 'Nominee'

    【Evidence】
    award title refers to title; nominated refers to result = 'Winner' or 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 T2.award FROM Episode AS T1 INNER JOIN Award AS T2 ON T1.episode_id = T2.episode_id WHERE T1.episode = 20 AND T2.result IN ('Winner', 'Nominee')