Triple
T31043728
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Academy Award for Best Actress for "Lady for a Day" |
E791066
|
entity |
| Predicate | awardYearOfFilm |
P137792
|
FINISHED |
| Object | 1933 |
—
|
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: 1933 | Statement: [Academy Award for Best Actress for "Lady for a Day", awardYearOfFilm, 1933]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: awardYearOfFilm Context triple: [Academy Award for Best Actress for "Lady for a Day", awardYearOfFilm, 1933]
-
A.
yearOfAwardCeremony
chosen
Indicates the specific year in which an award ceremony took place.
-
B.
awardedForYearOfRelease
Indicates that an award is given in recognition of a work based on the year in which that work was released.
-
C.
AcademyAwardsYear
Indicates the specific year in which the referenced Academy Awards event took place.
-
D.
awardNominationYear
Indicates the year in which an entity received a nomination for an award.
-
E.
returnedAwardYear
Indicates the year in which an award that had previously been given was returned or relinquished.
- 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_69f224ca2fa881908a3ac5fedf207b90 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f7bbf906d8819099020e548dd56bc9 |
completed | May 3, 2026, 9:19 p.m. |
| PD | Predicate disambiguation | batch_69f7b9a2dcf88190a7c9e109e41267be |
completed | May 3, 2026, 9:09 p.m. |
Created at: April 29, 2026, 8:59 p.m.