Triple
T29983887
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jane Wyman |
E761671
|
entity |
| Predicate | yearOfAcademyAwardForBestActress |
P48623
|
FINISHED |
| Object | 1949 |
—
|
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: 1949 | Statement: [Jane Wyman, yearOfAcademyAwardForBestActress, 1949]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: yearOfAcademyAwardForBestActress Context triple: [Jane Wyman, yearOfAcademyAwardForBestActress, 1949]
-
A.
numberOfAcademyAwardsForBestActress
Indicates the total count of Academy Awards received by an entity specifically in the Best Actress category.
-
B.
academyAwardForBestActress
Indicates that an entity received the Academy Award for Best Actress in a leading role.
-
C.
AcademyAwardsYear
chosen
Indicates the specific year in which the referenced Academy Awards event took place.
-
D.
yearOfAwardCeremony
Indicates the specific year in which an award ceremony took place.
-
E.
yearOfAwardCannesBestActress
Indicates the specific year in which an individual received the Cannes Film Festival Best Actress award.
- 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_69f2246851148190b8e76206db94b105 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69fb563aec448190875410fb1a3ed624 |
completed | May 6, 2026, 2:54 p.m. |
| PD | Predicate disambiguation | batch_69fb35b9ede881908aaae93a215525df |
completed | May 6, 2026, 12:36 p.m. |
Created at: April 29, 2026, 6:36 p.m.