Triple
T4627307
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clarence Brown |
E101128
|
entity |
| Predicate | numberOfAcademyAwardNominationsForBestDirector |
P51848
|
FINISHED |
| Object | 6 |
—
|
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: 6 | Statement: [Clarence Brown, numberOfAcademyAwardNominationsForBestDirector, 6]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfAcademyAwardNominationsForBestDirector Context triple: [Clarence Brown, numberOfAcademyAwardNominationsForBestDirector, 6]
-
A.
numberOfAcademyAwardsForBestDirector
Indicates the total count of Academy Awards received by a director for the Best Director category.
-
B.
awardCount_AcademyAwardForBestDirector
Indicates the number of Academy Awards for Best Director that have been received.
-
C.
mostNominationsFilm
Indicates that a film holds the highest number of nominations within a given set, context, or award event.
-
D.
academyAwardsNominationsCount
chosen
Indicates the number of times an entity has been nominated for an Academy Award.
-
E.
numberOfAcademyAwardsForBestActress
Indicates the total count of Academy Awards received by an entity specifically in the Best Actress category.
- 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_69bd43d0497c8190ac23c65c5804846a |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd5a2e9780819081add547c760abc9 |
completed | March 20, 2026, 2:31 p.m. |
| PD | Predicate disambiguation | batch_69bd5231db7c8190b38d4fdbad8bf842 |
completed | March 20, 2026, 1:57 p.m. |
Created at: March 20, 2026, 1:13 p.m.