Triple
T1271466
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Academy Award for Best Makeup and Hairstyling |
E15718
|
entity |
| Predicate | awardedForWorksReleasedIn |
P15480
|
FINISHED |
| Object | previous calendar year |
—
|
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: previous calendar year | Statement: [Academy Award for Best Makeup and Hairstyling, awardedForWorksReleasedIn, previous calendar year]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: awardedForWorksReleasedIn Context triple: [Academy Award for Best Makeup and Hairstyling, awardedForWorksReleasedIn, previous calendar year]
-
A.
awardedForYearOfRelease
chosen
Indicates that an award is given in recognition of a work based on the year in which that work was released.
-
B.
awardReceivedYear
Indicates the specific year in which an entity received a particular award.
-
C.
awardReceivedByWork
Indicates that a particular award was given in recognition of a specific work (such as a book, film, or artwork).
-
D.
notableAwardWork
Indicates that a work is the specific creation (e.g., book, film, artwork) for which an award or honor was given.
-
E.
awardedSince
Indicates that an entity has been receiving a particular award continuously from a specified starting time onward.
- 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_69a4935a94308190bb92555b79032824 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4c06ae7b88190a1e0b5232d84a7b1 |
completed | March 1, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69a4bede52a081909665d60acbe41d31 |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:50 p.m.