Triple
T33197871
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1936 Oscars |
E849814
|
entity |
| Predicate | notableFilmWithMostWins |
—
|
GENERATED |
| Object | The Informer |
—
|
UNRECOGNIZED GENERATED |
How this triple was built (1 step)
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.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableFilmWithMostWins Context triple: [1936 Oscars, notableFilmWithMostWins, The Informer]
-
A.
mostAwardsFilm
chosen
Indicates that a film is the one that has received the highest number of awards within a given set or context.
-
B.
mostNominationsFilm
Indicates that a film holds the highest number of nominations within a given set, context, or award event.
-
C.
mostAwardsFilmCount
Indicates the total number of awards received by the film that holds the record for having the most awards.
-
D.
academyAwardWins
Indicates that one entity has won a specified number of Academy Awards (Oscars) or that a winning relationship exists between the entity and the Academy Award.
-
E.
numberOfAcademyAwardsForBestDirector
Indicates the total count of Academy Awards received by a director for the Best Director category.
- F. None of above.
Provenance (1 batch)
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_69f3495efedc8190843a5728089544b9 |
completed | April 30, 2026, 12:21 p.m. |
Created at: May 1, 2026, 1:29 a.m.