Triple
T580074
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Katharine Hepburn |
E15036
|
entity |
| Predicate | numberOfAcademyAwardsForBestActress |
P15741
|
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: [Katharine Hepburn, numberOfAcademyAwardsForBestActress, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfAcademyAwardsForBestActress Context triple: [Katharine Hepburn, numberOfAcademyAwardsForBestActress, 4]
-
A.
academyAwardForBestActress
Indicates that an entity received the Academy Award for Best Actress in a leading role.
-
B.
academyAwardNominations
Indicates that an entity has received one or more nominations for an Academy Award (Oscars).
-
C.
oscarAward
Indicates that an entity has received or been honored with an Academy Award (Oscar).
-
D.
mostNominationsFilm
Indicates that a film holds the highest number of nominations within a given set, context, or award event.
-
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_69a4935783b8819082b77726ec10cc42 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a49b82b5248190ac863407e8207a3b |
completed | March 1, 2026, 8:03 p.m. |
| PD | Predicate disambiguation | batch_69a494c7f9008190bd8d05b4dc2a7c7f |
completed | March 1, 2026, 7:34 p.m. |
| PDg | Predicate description generation | batch_69a4985a2d08819090947895d9439e06 |
completed | March 1, 2026, 7:49 p.m. |
Created at: March 1, 2026, 7:33 p.m.