You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 simpson_episodes
【Schema】
# Table: Credit
[
(episode_id:TEXT, Examples: [S20-E10, S20-E13, S20-E14]),
(category:TEXT, Examples: [Casting Department, Animation Department, Sound Department]),
(person:TEXT, Examples: [Bonita Pietila, Adam Kuhlman, Carlton Batten]),
(role:TEXT, Examples: [casting, additional timer, adr engineer]),
(credited:TEXT, Examples: [true, false])
]
# Table: Award
[
(award_id:INTEGER, Primary Key, Examples: [325, 326, 327]),
(organization:TEXT, Examples: [Primetime Emmy Awards]),
(year:INTEGER, Examples: [2009, 2010, 2017]),
(award_category:TEXT, Examples: [Primetime Emmy, Annie, Image Award]),
(award:TEXT),
(person:TEXT, Examples: [Dan Castellaneta, Hank Azaria, Harry Shearer]),
(role:TEXT, Examples: [executive producer]),
(episode_id:TEXT, Examples: [S20-E18, S20-E16, S20-E8]),
(season:TEXT),
(song:TEXT),
(result:TEXT, Examples: [Winner, Nominee])
]
# Table: Person
[
(name:TEXT, Primary Key, Examples: [Adam Greeley, Adam Klein, Adam Kuhlman]),
(birthdate:TEXT, Examples: [1963-05-04, 1975-03-30, 1969-12-04]),
(birth_name:TEXT, Examples: [Marc Edward Wilmore, Valentina Lantigua, Joseph Stewart Burns]),
(birth_place:TEXT, Examples: [USA, Brooklyn, Chicago]),
(birth_region:TEXT, Examples: [California, New York, Illinois]),
(birth_country:TEXT, Examples: [USA, Canada, Ireland]),
(height_meters:REAL, Examples: [1.85, 1.75, 2.03]),
(nickname:TEXT, Examples: [Jim, Doofus, Hoops])
]
# Table: Vote
[
(episode_id:TEXT, Examples: [S20-E1, S20-E10, S20-E11]),
(stars:INTEGER, Examples: [2, 3, 4]),
(votes:INTEGER, Examples: [16, 20, 36]),
(percent:REAL, Examples: [1.3, 1.7, 3.0])
]
# Table: Character_Award
[
(award_id:INTEGER, Examples: [325, 326, 327]),
(character:TEXT, Examples: [Homer Simpson, Moe Szyslak, Kent Brockman])
]
# Table: Episode
[
(episode_id:TEXT, Primary Key, Examples: [S20-E1, S20-E10, S20-E11]),
(season:INTEGER, Examples: [20]),
(episode:INTEGER, Examples: [1, 2, 3]),
(number_in_series:INTEGER, Examples: [421, 422, 423]),
(title:TEXT, Examples: [Sex, Pies and Idiot Scrapes]),
(summary:TEXT),
(air_date:TEXT, Examples: [2008-09-28, 2008-10-05, 2008-10-19]),
(episode_image:TEXT),
(rating:REAL, Examples: [7.2, 7.0, 7.1]),
(votes:INTEGER, Examples: [1192, 1055, 1015])
]
# Table: Keyword
[
(episode_id:TEXT, Primary Key, Examples: [S20-E1, S20-E10, S20-E11]),
(keyword:TEXT, Primary Key, Examples: [1930s to 2020s, erotic bakery, cake])
]
【Foreign keys】
Credit.episode_id=Episode.episode_id
Credit.person=Person.name
Award.person=Person.name
Award.episode_id=Episode.episode_id
Vote.episode_id=Episode.episode_id
Character_Award.award_id=Award.award_id
Keyword.episode_id=Episode.episode_id

    【Question】
    Mention the episode ID, title and any three keywords of the episode which get the most number of votes in star classification of worst. star classification of worst refers to stars = 1; most number of votes refers to MAX(votes)

    【Evidence】
    star classification of worst refers to stars = 1; most number of votes refers to MAX(votes)

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

SQL: SELECT DISTINCT T3.episode_id, T2.title, T1.keyword FROM Keyword AS T1 INNER JOIN Episode AS T2 ON T1.episode_id = T2.episode_id INNER JOIN Vote AS T3 ON T2.episode_id = T3.episode_id WHERE T3.stars = 1 ORDER BY T3.votes DESC LIMIT 3;