Liar Liar
E102865
Liar Liar is a 1997 comedy film starring Jim Carrey as a fast-talking lawyer magically compelled to tell the truth for 24 hours, leading to a series of chaotic and humorous consequences.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Liar Liar canonical | 12 |
How this entity was disambiguated
This entity first appeared as the object of triple T880248 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
Target entity: Liar Liar Context triple: [Brian Grazer, notableWork, Liar Liar]
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A.
True Lies
True Lies is a 1994 action-comedy film directed by James Cameron, starring Arnold Schwarzenegger as a secret agent leading a double life.
-
B.
The Jerk
The Jerk is a 1979 American comedy film starring Steve Martin as a naive, eccentric man whose rags-to-riches-to-rags journey showcases his signature absurdist humor.
-
C.
Ruthless People
Ruthless People is a 1986 dark comedy film about a botched kidnapping plot, starring Bette Midler, Danny DeVito, and Judge Reinhold.
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D.
Big Daddy
Big Daddy is a 1999 comedy film starring Adam Sandler as an immature man who unexpectedly becomes the guardian of a young boy, leading to humorous and heartfelt life lessons.
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E.
Behind the Candelabra
Behind the Candelabra is a 2013 biographical drama film about pianist Liberace’s secret relationship with Scott Thorson, starring Michael Douglas and Matt Damon and directed by Steven Soderbergh.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Target entity: Liar Liar Target entity description: Liar Liar is a 1997 comedy film starring Jim Carrey as a fast-talking lawyer magically compelled to tell the truth for 24 hours, leading to a series of chaotic and humorous consequences.
-
A.
True Lies
True Lies is a 1994 action-comedy film directed by James Cameron, starring Arnold Schwarzenegger as a secret agent leading a double life.
-
B.
The Jerk
The Jerk is a 1979 American comedy film starring Steve Martin as a naive, eccentric man whose rags-to-riches-to-rags journey showcases his signature absurdist humor.
-
C.
Ruthless People
Ruthless People is a 1986 dark comedy film about a botched kidnapping plot, starring Bette Midler, Danny DeVito, and Judge Reinhold.
-
D.
Big Daddy
Big Daddy is a 1999 comedy film starring Adam Sandler as an immature man who unexpectedly becomes the guardian of a young boy, leading to humorous and heartfelt life lessons.
-
E.
Behind the Candelabra
Behind the Candelabra is a 2013 biographical drama film about pianist Liberace’s secret relationship with Scott Thorson, starring Michael Douglas and Matt Damon and directed by Steven Soderbergh.
- F. None of above. chosen
Statements (44)
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Subject: Liar Liar Description of subject: Liar Liar is a 1997 comedy film starring Jim Carrey as a fast-talking lawyer magically compelled to tell the truth for 24 hours, leading to a series of chaotic and humorous consequences.
Referenced by (12)
Full triples — surface form annotated when it differs from this entity's canonical label.