Triple
T3614345
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frances McDormand |
E76562
|
entity |
| Predicate | academyAwardsBestPictureCount |
P50420
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [Frances McDormand, academyAwardsBestPictureCount, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: academyAwardsBestPictureCount Context triple: [Frances McDormand, academyAwardsBestPictureCount, 1]
-
A.
numberOfAcademyAwardsForBestDirector
Indicates the total count of Academy Awards received by a director for the Best Director category.
-
B.
oscarBestPictureYear
Indicates the year in which a given film received the Academy Award for Best Picture.
-
C.
awardCount_AcademyAwardForBestDirector
Indicates the number of Academy Awards for Best Director that have been received.
-
D.
academyAwardWins
Indicates that one entity has won a specified number of Academy Awards (Oscars) or that a winning relationship exists between the entity and the Academy Award.
-
E.
mostNominationsFilm
Indicates that a film holds the highest number of nominations within a given set, context, or award event.
- 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_69ad85da0ba481908b3b48c69efe2b98 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc279f6948190b9655a4cf3e77d89 |
completed | March 8, 2026, 6:39 p.m. |
| PD | Predicate disambiguation | batch_69adb83f1e4c8190ab501c1c05b14c08 |
completed | March 8, 2026, 5:56 p.m. |
| PDg | Predicate description generation | batch_69adb9bbb62c8190989629ca11733e1b |
completed | March 8, 2026, 6:02 p.m. |
Created at: March 8, 2026, 3:23 p.m.