Triple
T552515
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BAFTA Award for Best Supporting Actress |
E11870
|
entity |
| Predicate | firstAwardedForFilmYear |
P124
|
FINISHED |
| Object | 1967 |
—
|
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: 1967 | Statement: [BAFTA Award for Best Supporting Actress, firstAwardedForFilmYear, 1967]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstAwardedForFilmYear Context triple: [BAFTA Award for Best Supporting Actress, firstAwardedForFilmYear, 1967]
-
A.
firstAwarded
chosen
Indicates the time or occasion when an award, honor, or recognition was given for the very first time.
-
B.
awardReceivedYear
Indicates the specific year in which an entity received a particular award.
-
C.
firstWinnerYear
Indicates the year in which an entity first won a particular competition, award, or title.
-
D.
oscarBestPictureYear
Indicates the year in which a given film received the Academy Award for Best Picture.
-
E.
goldenSpikesAwardYear
Indicates the year in which a Golden Spikes Award was given or associated with a particular recipient or event.
- 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_69a4932941d08190815efd422f0b4ca7 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a499047bd4819089ca8345f1b6e46c |
completed | March 1, 2026, 7:52 p.m. |
| PD | Predicate disambiguation | batch_69a494bae210819093c2e0d33a8ca51a |
completed | March 1, 2026, 7:34 p.m. |
Created at: March 1, 2026, 7:32 p.m.