Triple
T32647296
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 73rd Golden Globe Awards |
E834628
|
entity |
| Predicate | bestScreenplayMotionPicture |
P10676
|
FINISHED |
| Object | Aaron Sorkin |
—
|
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: Aaron Sorkin | Statement: [73rd Golden Globe Awards, bestScreenplayMotionPicture, Aaron Sorkin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bestScreenplayMotionPicture Context triple: [73rd Golden Globe Awards, bestScreenplayMotionPicture, Aaron Sorkin]
-
A.
bestAdaptedScreenplayWinner
Indicates that the subject is the work or person that won the award for Best Adapted Screenplay in a given context or event.
-
B.
bestWritingMotionPictureStoryWinnerFilm
Indicates that the subject film is the winner of the Best Writing (Motion Picture Story) award.
-
C.
bestOriginalScreenplayWinner
chosen
Indicates that the subject is the work (typically a film) that won the award for Best Original Screenplay in a given context or year.
-
D.
bestStoryAndScreenplayMusicalOrComedyWinner
Indicates that the subject is the winner of the award for best story and screenplay in the musical or comedy category.
-
E.
bestScreenplayWriter
Indicates that the subject is recognized as the top or most outstanding writer of a screenplay, typically in comparison to other writers within a specific context or competition.
- 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_69f6f8164698819090c1b471f1caa4c6 |
completed | May 3, 2026, 7:24 a.m. |
| PD | Predicate disambiguation | batch_69f6f6619404819084662aef1238261c |
completed | May 3, 2026, 7:16 a.m. |
Created at: May 1, 2026, 1:07 a.m.