Triple
T473530
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Walt Disney |
E9009
|
entity |
| Predicate | numberOfAcademyHonoraryAwardsWon |
P3160
|
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: [Walt Disney, numberOfAcademyHonoraryAwardsWon, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfAcademyHonoraryAwardsWon Context triple: [Walt Disney, numberOfAcademyHonoraryAwardsWon, 4]
-
A.
numberOfAwards
chosen
Indicates the total count of awards that have been received by an entity.
-
B.
includesHonoraryAwardsFor
Indicates that one entity contains or lists honorary awards that have been granted to another entity.
-
C.
academyAwardNominations
Indicates that an entity has received one or more nominations for an Academy Award (Oscars).
-
D.
mostNominationsCount
Indicates the highest number of nominations that any entity in the relevant set has 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.
Provenance (3 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_69a2e7ff81708190b0507a24a997232c |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2f0208c788190a96cdabcf593fda7 |
completed | Feb. 28, 2026, 1:39 p.m. |
| PD | Predicate disambiguation | batch_69a2edecefb081908331ef8b9edf6636 |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.