Triple
T14190887
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Purple Haze 2 |
E351708
|
entity |
| Predicate | hasContributor |
P4244
|
FINISHED |
| Object | Shooter |
E890119
|
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: Shooter | Statement: [Purple Haze 2, hasContributor, Shooter]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shooter Context triple: [Purple Haze 2, hasContributor, Shooter]
-
A.
Shooter
"Shooter" is a 2007 American action thriller film, based on Stephen Hunter's novel "Point of Impact," about a former Marine sniper framed for an assassination plot.
-
B.
Shooter
chosen
Shooter is the stage name of American country rock musician and producer Shooter Jennings, known for blending outlaw country with rock influences.
-
C.
Shooter
"Shooter" is a track from Lil Wayne's album *Tha Carter II*, known for its introspective lyrics and melodic, laid-back production.
-
D.
Killshot
Killshot is a crime novel by Elmore Leonard that follows a married couple targeted by a pair of hitmen, blending dark humor with tense, character-driven suspense.
-
E.
Shoot to Kill
Shoot to Kill is a 1988 American thriller film about an FBI agent and a mountain guide tracking a murderous criminal through the wilderness.
- 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_69d827894ac0819097803e57f3227b23 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61df628c8190ba3f557e2128dce5 |
completed | April 14, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd1946eb68819096adf3c16a39818d |
completed | May 7, 2026, 10:59 p.m. |
Created at: April 10, 2026, 1:04 a.m.