Triple
T837696
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frank Capra |
E18106
|
entity |
| Predicate | numberOfAcademyAwardsForBestDirector |
P20499
|
FINISHED |
| Object | 3 |
—
|
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: 3 | Statement: [Frank Capra, numberOfAcademyAwardsForBestDirector, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfAcademyAwardsForBestDirector Context triple: [Frank Capra, numberOfAcademyAwardsForBestDirector, 3]
-
A.
numberOfAcademyAwardsForBestActress
Indicates the total count of Academy Awards received by an entity specifically in the Best Actress category.
-
B.
mostNominationsFilm
Indicates that a film holds the highest number of nominations within a given set, context, or award event.
-
C.
mostAwardsFilm
Indicates that a film is the one that has received the highest number of awards within a given set or context.
-
D.
bestDirectorWinner
Indicates that the subject is the winner of a "Best Director" award for the object (such as a specific film, event, or year).
-
E.
bestDirectorFilm
Indicates that a film is the work for which a director received a Best Director award.
- F. None of above. chosen
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_69a49389f44881909a608fb27d89f247 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4abd0e8bc8190afe29cd4745c2f86 |
completed | March 1, 2026, 9:12 p.m. |
| PD | Predicate disambiguation | batch_69a4aa7dfc5c8190890c9df485d73a86 |
completed | March 1, 2026, 9:07 p.m. |
| PDg | Predicate description generation | batch_69a4ab4893e481908632102d240466dc |
completed | March 1, 2026, 9:10 p.m. |
Created at: March 1, 2026, 7:38 p.m.