Just Cause
E554359
Just Cause is a 1995 legal thriller film starring Sean Connery and Kate Capshaw, centered on a Harvard law professor investigating a potentially wrongful murder conviction in the American South.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Just Cause canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T5897552 — 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: Just Cause Context triple: [Kate Capshaw, notableWork, Just Cause]
-
A.
Just Cause Y’all Waited 2
Just Cause Y’all Waited 2 is a 2020 mixtape by Chicago rapper Lil Durk that blends melodic drill with introspective street narratives and helped solidify his mainstream rise.
-
B.
Grand Theft Auto V
Grand Theft Auto V is an open-world action-adventure video game set in the fictional state of San Andreas, renowned for its expansive sandbox gameplay, multi-protagonist story, and enduring popularity in both single-player and online modes.
-
C.
The Crew
The Crew is the popular nickname for the Columbus Crew, a Major League Soccer club based in Columbus, Ohio.
-
D.
Steep
Steep is a rural village and civil parish in East Hampshire, England, known for its scenic countryside and literary associations with poet Edward Thomas.
-
E.
Red Dead Redemption 2
Red Dead Redemption 2 is an open-world action-adventure game by Rockstar Games set in a richly detailed, late-1800s American frontier, following outlaw Arthur Morgan and the Van der Linde gang as they struggle with loyalty, survival, and the decline of the Wild West.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Target entity: Just Cause Target entity description: Just Cause is a 1995 legal thriller film starring Sean Connery and Kate Capshaw, centered on a Harvard law professor investigating a potentially wrongful murder conviction in the American South.
-
A.
Just Cause Y’all Waited 2
Just Cause Y’all Waited 2 is a 2020 mixtape by Chicago rapper Lil Durk that blends melodic drill with introspective street narratives and helped solidify his mainstream rise.
-
B.
Grand Theft Auto V
Grand Theft Auto V is an open-world action-adventure video game set in the fictional state of San Andreas, renowned for its expansive sandbox gameplay, multi-protagonist story, and enduring popularity in both single-player and online modes.
-
C.
The Crew
The Crew is the popular nickname for the Columbus Crew, a Major League Soccer club based in Columbus, Ohio.
-
D.
Steep
Steep is a rural village and civil parish in East Hampshire, England, known for its scenic countryside and literary associations with poet Edward Thomas.
-
E.
Red Dead Redemption 2
Red Dead Redemption 2 is an open-world action-adventure game by Rockstar Games set in a richly detailed, late-1800s American frontier, following outlaw Arthur Morgan and the Van der Linde gang as they struggle with loyalty, survival, and the decline of the Wild West.
- F. None of above. chosen
Statements (44)
| Predicate | Object |
|---|---|
| instanceOf |
film
ⓘ
legal thriller film ⓘ |
| basedOn | Just Cause (novel) NERFINISHED ⓘ |
| basedOnAuthor | John Katzenbach NERFINISHED ⓘ |
| character |
Blair Sullivan
NERFINISHED
ⓘ
Bobby Earl Ferguson NERFINISHED ⓘ Laurie Armstrong NERFINISHED ⓘ Paul Armstrong NERFINISHED ⓘ Sheriff Tanny Brown NERFINISHED ⓘ |
| cinematographyBy | László Kovács NERFINISHED ⓘ |
| countryOfOrigin |
United States of America
ⓘ
surface form:
United States
|
| director | Arne Glimcher NERFINISHED ⓘ |
| distributor |
Warner Bros. Pictures
ⓘ
surface form:
Warner Bros.
|
| editedBy | Peter E. Berger NERFINISHED ⓘ |
| filmingLocation | Georgia NERFINISHED ⓘ |
| genre |
crime drama film
ⓘ
legal thriller film ⓘ |
| mainProtagonist | Paul Armstrong NERFINISHED ⓘ |
| medium | theatrical film ⓘ |
| mpaaRating | R ⓘ |
| musicBy | James Newton Howard ⓘ |
| narrativeForm | feature-length narrative film ⓘ |
| originalLanguage | English ⓘ |
| plotSummary | A Harvard law professor investigates a potentially wrongful murder conviction in the American South. ⓘ |
| producer |
Arne Glimcher
NERFINISHED
ⓘ
Lee Rich NERFINISHED ⓘ |
| productionCompany | Lee Rich Productions NERFINISHED ⓘ |
| protagonistOccupation | Harvard law professor ⓘ |
| releaseYear | 1995 ⓘ |
| runtimeMinutes | 102 ⓘ |
| screenwriter |
Jeb Stuart
NERFINISHED
ⓘ
Peter Stone NERFINISHED ⓘ |
| settingLocation | Florida NERFINISHED ⓘ |
| starredActor |
Blair Underwood
NERFINISHED
ⓘ
Ed Harris NERFINISHED ⓘ Kate Capshaw NERFINISHED ⓘ Laurence Fishburne NERFINISHED ⓘ Ruby Dee NERFINISHED ⓘ Scarlett Johansson NERFINISHED ⓘ Sean Connery NERFINISHED ⓘ |
| theme |
capital punishment
ⓘ
racial injustice ⓘ wrongful conviction ⓘ |
| title | Just Cause NERFINISHED ⓘ |
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: Just Cause Description of subject: Just Cause is a 1995 legal thriller film starring Sean Connery and Kate Capshaw, centered on a Harvard law professor investigating a potentially wrongful murder conviction in the American South.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.