Triple
T971791
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BAFTA Award for Best Actress in a Leading Role |
E20959
|
entity |
| Predicate | firstAwardedForFilmsOfYear |
P15480
|
FINISHED |
| Object | 1951 |
—
|
LITERAL 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: 1951 | Statement: [BAFTA Award for Best Actress in a Leading Role, firstAwardedForFilmsOfYear, 1951]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstAwardedForFilmsOfYear Context triple: [BAFTA Award for Best Actress in a Leading Role, firstAwardedForFilmsOfYear, 1951]
-
A.
awardedForYearOfRelease
chosen
Indicates that an award is given in recognition of a work based on the year in which that work was released.
-
B.
firstAwardedForFilm
Indicates the film for which an entity (such as a person or award) was first given or received an award.
-
C.
oscarBestPictureYear
Indicates the year in which a given film received the Academy Award for Best Picture.
-
D.
mostAwardsFilm
Indicates that a film is the one that has received the highest number of awards within a given set or context.
-
E.
oscarAward
Indicates that an entity has received or been honored with an Academy Award (Oscar).
- 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_69a493b33d2c81909c52c369d3ca8436 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b44c38f08190997e141d424e9e04 |
completed | March 1, 2026, 9:49 p.m. |
| PD | Predicate disambiguation | batch_69a4b2a6aa2c8190aebba71320ab678f |
completed | March 1, 2026, 9:41 p.m. |
Created at: March 1, 2026, 7:40 p.m.