Triple
T21944285
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Inferno (2016 film) |
E541895
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Tom Elkins |
—
|
NE NERFINISHED |
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: Tom Elkins | Statement: [Inferno (2016 film), editedBy, Tom Elkins]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tom Elkins Context triple: [Inferno (2016 film), editedBy, Tom Elkins]
-
A.
Tom Elkins
chosen
Tom Elkins is a film editor best known for his work in the horror and thriller genres, including editing movies like "Inferno."
-
B.
Tom Elliott
Tom Elliott is a Northern Irish Ulster Unionist Party politician who has served as the Member of Parliament for the Fermanagh and South Tyrone constituency.
-
C.
Dan Harkins
Dan Harkins is a computer security researcher and cryptographer known for designing and contributing to widely used key exchange and authentication protocols.
-
D.
Jim Barnhill
Jim Barnhill was an American football official best known for serving as a referee in the American Football League during the 1960s.
-
E.
Bill Wittliff
Bill Wittliff was an American screenwriter, author, and photographer best known for adapting and writing acclaimed Western-themed films and television miniseries.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c47e2e5c81909a7f74ce3de50911 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f1242688988190a7b8f033c49368de |
completed | April 28, 2026, 9:18 p.m. |
Created at: April 16, 2026, 7:56 p.m.