Triple
T2644655
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Emmanuel Lubezki |
E62955
|
entity |
| Predicate | numberOfAcademyAwardsForBestCinematography |
P41011
|
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: [Emmanuel Lubezki, numberOfAcademyAwardsForBestCinematography, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfAcademyAwardsForBestCinematography Context triple: [Emmanuel Lubezki, numberOfAcademyAwardsForBestCinematography, 3]
-
A.
cinematographyAwardedTo
Indicates that a cinematography-related award has been given to a particular recipient (such as a person or team) for their work.
-
B.
bestCinematographyWinner
Indicates that the subject is the work or individual that won the award for best cinematography in a given context or event.
-
C.
numberOfAcademyAwardsForBestDirector
Indicates the total count of Academy Awards received by a director for the Best Director category.
-
D.
awardCount_AcademyAwardForBestDirector
Indicates the number of Academy Awards for Best Director that have been received.
-
E.
mostAwardsFilm
Indicates that a film is the one that has received the highest number of awards within a given set or context.
- 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_69ab4c3f2dcc819082df80f5e032f690 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abd917192081908e7a2cf780a17b83 |
completed | March 7, 2026, 7:51 a.m. |
| PD | Predicate disambiguation | batch_69abd814298c8190952f05aed43f6bb8 |
completed | March 7, 2026, 7:47 a.m. |
| PDg | Predicate description generation | batch_69abd879bb808190bd2c34de1664c816 |
completed | March 7, 2026, 7:49 a.m. |
Created at: March 6, 2026, 9:53 p.m.