Triple
T5143073
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mario F. Kassar |
E116004
|
entity |
| Predicate | produced |
P490
|
FINISHED |
| Object | Red Heat |
E308003
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Red Heat | Statement: [Mario F. Kassar, produced, Red Heat]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Red Heat Context triple: [Mario F. Kassar, produced, Red Heat]
-
A.
Red Heat
chosen
Red Heat is a 1988 buddy-cop action film starring Arnold Schwarzenegger and James Belushi, directed by Walter Hill.
-
B.
Hard Target
Hard Target is a 1993 American action film directed by John Woo and starring Jean-Claude Van Damme, known for its stylized violence and Woo’s Hollywood debut.
-
C.
Homicide Squad
The Homicide Squad is a specialized New York City Police Department unit dedicated to investigating and solving murder cases.
-
D.
Crime Pays
Crime Pays is a 2009 studio album by Harlem rapper Cam'ron, known for its gritty street narratives and return-to-form sound.
-
E.
License to Kill
"License to Kill" is a 1989 James Bond film best known for its darker tone and for featuring the title song performed by soul singer Gladys Knight.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69bd4446c0e08190a7c29dc74976bf03 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7883004881909c763da818d9b6e2 |
completed | March 20, 2026, 4:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69becfec5c108190a3882c25118179a7 |
completed | March 21, 2026, 5:05 p.m. |
Created at: March 20, 2026, 1:43 p.m.