Triple
T21536732
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Crossing Over |
E531367
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Arthur Coburn |
—
|
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: Arthur Coburn | Statement: [Crossing Over, editedBy, Arthur Coburn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Arthur Coburn Context triple: [Crossing Over, editedBy, Arthur Coburn]
-
A.
Arthur Coburn
Arthur Coburn is a film editor best known for his work on major Hollywood productions, including the action-comedy classic "Beverly Hills Cop."
-
B.
Arthur Coburn
Arthur Coburn is a film editor best known for his work on the 1994 Jim Carrey comedy "The Mask."
-
C.
Arthur Coburn
Arthur Coburn is a film editor best known for his work on the movie "The Cooler."
-
D.
Warren William
Warren William was an American stage and film actor of the 1930s, best known for his suave, often morally ambiguous leading and supporting roles in Hollywood pre-Code dramas and mysteries.
-
E.
Charles Bickford
Charles Bickford was an American character actor known for his rugged screen presence and acclaimed supporting roles in numerous classic Hollywood films.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
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_69e0c45e5b8881908ac18fc2f493b114 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ee9d0e5a9c8190894ec3666d3296aa |
completed | April 26, 2026, 11:17 p.m. |
Created at: April 16, 2026, 6:27 p.m.