Triple
T7011660
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Isabelle Adjani |
E162595
|
entity |
| Predicate | numberOfCésarAwardsForBestActress |
P73652
|
FINISHED |
| Object | 5 |
—
|
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: 5 | Statement: [Isabelle Adjani, numberOfCésarAwardsForBestActress, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfCésarAwardsForBestActress Context triple: [Isabelle Adjani, numberOfCésarAwardsForBestActress, 5]
-
A.
yearOfAwardCannesBestActress
Indicates the specific year in which an individual received the Cannes Film Festival Best Actress award.
-
B.
numberOfAcademyAwardsForBestActress
Indicates the total count of Academy Awards received by an entity specifically in the Best Actress category.
-
C.
academyAwardForBestActress
Indicates that an entity received the Academy Award for Best Actress in a leading role.
-
D.
bestActressWinner
Indicates that the subject has won the Best Actress award in a given competition or context.
-
E.
leadActress
Indicates that the subject is the primary female performer in the specified film, show, or production.
- 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_69c6885a127c8190867b059bdccf13ff |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dc5729448190af66dbd6f3e8936e |
completed | March 27, 2026, 7:36 p.m. |
| PD | Predicate disambiguation | batch_69c6d7c790288190b7cbbaa4a5f9c91d |
completed | March 27, 2026, 7:17 p.m. |
| PDg | Predicate description generation | batch_69c6d8a4930081908f1ae1e6ca8a514c |
completed | March 27, 2026, 7:21 p.m. |
Created at: March 27, 2026, 2:34 p.m.