Triple
T3564581
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Starsky & Hutch |
E75413
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Vince Vaughn |
E257907
|
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: Vince Vaughn | Statement: [Starsky & Hutch, starring, Vince Vaughn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vince Vaughn Context triple: [Starsky & Hutch, starring, Vince Vaughn]
-
A.
Vince Vaughn
chosen
Vince Vaughn is an American actor and comedian known for his roles in hit comedies such as "Wedding Crashers," "Dodgeball," and "Old School."
-
B.
Owen Wilson
Owen Wilson is an American actor and screenwriter known for his laid-back charm and roles in popular comedies and adventure films such as "Wedding Crashers," "Zoolander," and "Midnight in Paris."
-
C.
Chris Penn
Chris Penn was an American character actor known for his roles in films such as "Reservoir Dogs," "Footloose," and "True Romance."
-
D.
Kevin James
Kevin James is an American actor and comedian best known for starring in the sitcom "The King of Queens" and films such as "Paul Blart: Mall Cop."
-
E.
Luke Wilson
Luke Wilson is an American actor known for his roles in films such as "The Royal Tenenbaums," "Old School," and "Legally Blonde."
- 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_69ad85d512708190829c8b2d3a2ccfb8 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc0a76c008190a2056b6d990776fe |
completed | March 8, 2026, 6:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b44ef5f5348190b1088edee0d9d55c |
completed | March 13, 2026, 5:52 p.m. |
Created at: March 8, 2026, 3:21 p.m.