Triple
T35031313
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 69th Academy Awards |
E1010489
|
entity |
| Predicate | bestSoundEffectsEditingWinner |
P36454
|
FINISHED |
| Object | The Ghost and the Darkness |
—
|
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: The Ghost and the Darkness | Statement: [69th Academy Awards, bestSoundEffectsEditingWinner, The Ghost and the Darkness]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bestSoundEffectsEditingWinner Context triple: [69th Academy Awards, bestSoundEffectsEditingWinner, The Ghost and the Darkness]
-
A.
bestSoundEditingWinner
chosen
Indicates that the subject is the winner of an award for best sound editing in a given context or event.
-
B.
bestEditingWinner
Indicates that the subject is the winner of an award or recognition for best editing in a given context or competition.
-
C.
bestSoundRecordingWinner
Indicates that one entity is the winner of an award for best sound recording in relation to another entity (such as a work, event, or year).
-
D.
bestSoundMixingWinner
Indicates that the subject is the winner of an award or recognition for best sound mixing.
-
E.
bestVisualEffectsWinner
Indicates that the subject is the work or individual that won the award for best visual effects in a given 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_69f76dccf0108190af43b465d3750196 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f7858aa5508190a07dde993b3356fc |
completed | May 3, 2026, 5:27 p.m. |
| PD | Predicate disambiguation | batch_69f7841812f081909d878955d114088e |
completed | May 3, 2026, 5:21 p.m. |
Created at: May 3, 2026, 4:01 p.m.