Triple
T33197870
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1936 Oscars |
E849814
|
entity |
| Predicate | notableFilmWithMostNominations |
P8122
|
FINISHED |
| Object | Mutiny on the Bounty |
E72141
|
NE FINISHED |
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: Mutiny on the Bounty | Statement: [1936 Oscars, notableFilmWithMostNominations, Mutiny on the Bounty]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableFilmWithMostNominations Context triple: [1936 Oscars, notableFilmWithMostNominations, Mutiny on the Bounty]
-
A.
mostNominationsFilm
chosen
Indicates that a film holds the highest number of nominations within a given set, context, or award event.
-
B.
mostNominationsCount
Indicates the highest number of nominations that any entity in the relevant set has received.
-
C.
mostAwardsFilm
Indicates that a film is the one that has received the highest number of awards within a given set or context.
-
D.
mostAwardsFilmCount
Indicates the total number of awards received by the film that holds the record for having the most awards.
-
E.
mostNominationsRecipient
Indicates that the subject is the entity that has received the highest number of nominations within a given context or set.
- F. None of above.
Provenance (4 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_69f3495efedc8190843a5728089544b9 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a037c894b488190bcbec2eccaff4a01 |
completed | May 12, 2026, 7:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a352fbdf2a88190bd74b039915ae0f1 |
completed | June 19, 2026, 12:02 p.m. |
| PD | Predicate disambiguation | batch_6a0379f338b881908e5593e45d764f4d |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 1:29 a.m.