Triple
T32647282
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 73rd Golden Globe Awards |
E834628
|
entity |
| Predicate | bestActorMotionPictureDrama |
P8115
|
FINISHED |
| Object | Leonardo DiCaprio |
—
|
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: Leonardo DiCaprio | Statement: [73rd Golden Globe Awards, bestActorMotionPictureDrama, Leonardo DiCaprio]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bestActorMotionPictureDrama Context triple: [73rd Golden Globe Awards, bestActorMotionPictureDrama, Leonardo DiCaprio]
-
A.
bestActorMotionPictureDramaWork
Indicates that a person received the award for best actor in a motion picture drama for a specific work.
-
B.
bestActorMotionPictureMusicalOrComedyWork
Indicates that an entity received the Best Actor in a Motion Picture – Musical or Comedy award for a specific work.
-
C.
bestActressMotionPictureDramaWork
Indicates that a work is associated with winning or being awarded the Best Actress in a Motion Picture – Drama honor.
-
D.
bestActorWinner
chosen
Indicates that the subject is the recipient of a "Best Actor" award for a particular performance or event.
-
E.
bestActressMotionPictureMusicalOrComedyWork
Indicates that a work received the Golden Globe award for Best Actress in a Motion Picture – Musical or Comedy.
- F. None of above.
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_69f3492e773c81908afc10651e46cad3 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6c775d6188190b236fc4f89a11b61 |
completed | May 3, 2026, 3:56 a.m. |
| PD | Predicate disambiguation | batch_69f6c3f617c08190a70ba880210f908c |
completed | May 3, 2026, 3:41 a.m. |
Created at: May 1, 2026, 1:07 a.m.