Triple
T32647286
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 73rd Golden Globe Awards |
E834628
|
entity |
| Predicate | bestActorMotionPictureMusicalOrComedy |
P91490
|
FINISHED |
| Object | Matt Damon |
—
|
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: Matt Damon | Statement: [73rd Golden Globe Awards, bestActorMotionPictureMusicalOrComedy, Matt Damon]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bestActorMotionPictureMusicalOrComedy Context triple: [73rd Golden Globe Awards, bestActorMotionPictureMusicalOrComedy, Matt Damon]
-
A.
bestActorMotionPictureMusicalOrComedyWork
chosen
Indicates that an entity received the Best Actor in a Motion Picture – Musical or Comedy award for a specific work.
-
B.
bestActressMotionPictureMusicalOrComedyWork
Indicates that a work received the Golden Globe award for Best Actress in a Motion Picture – Musical or Comedy.
-
C.
bestActorMotionPictureDramaWork
Indicates that a person received the award for best actor in a motion picture drama for a specific work.
-
D.
bestActorWinner
Indicates that the subject is the recipient of a "Best Actor" award for a particular performance or event.
-
E.
bestScoringOfADramaticOrComedyPictureWinner
Indicates that the subject is the winner for best scoring of a dramatic or comedy motion picture.
- 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_69f6d16f5cb881908eed141afaaa0b51 |
completed | May 3, 2026, 4:39 a.m. |
| PD | Predicate disambiguation | batch_69f6cfe45554819089cbbd538d992132 |
completed | May 3, 2026, 4:32 a.m. |
Created at: May 1, 2026, 1:07 a.m.