Triple
T802775
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Ford |
E17163
|
entity |
| Predicate | awardCount_AcademyAwardForBestDirector |
P21203
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [John Ford, awardCount_AcademyAwardForBestDirector, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: awardCount_AcademyAwardForBestDirector Context triple: [John Ford, awardCount_AcademyAwardForBestDirector, 4]
-
A.
bestDirectorWinner
Indicates that the subject is the winner of a "Best Director" award for the object (such as a specific film, event, or year).
-
B.
oscarAward
Indicates that an entity has received or been honored with an Academy Award (Oscar).
-
C.
bestPictureWinner
Indicates that the subject is the film that won the Best Picture award in a given context or year.
-
D.
mostAwardsFilm
Indicates that a film is the one that has received the highest number of awards within a given set or context.
-
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_69a49378b9c48190adbf5f62e5b7aca1 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4ace495348190aec66f35ea90bc89 |
completed | March 1, 2026, 9:17 p.m. |
| PD | Predicate disambiguation | batch_69a4aa70973c8190adbf08302d1103a9 |
completed | March 1, 2026, 9:06 p.m. |
| PDg | Predicate description generation | batch_69a4ace369b481908ad69de6de99f5e6 |
completed | March 1, 2026, 9:17 p.m. |
Created at: March 1, 2026, 7:38 p.m.