Triple
T23978384
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Academy Award for Best Supporting Actor for The Wolf of Wall Street |
E604436
|
entity |
| Predicate | forPerformerGender |
P20803
|
FINISHED |
| Object | male actor |
—
|
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: male actor | Statement: [Academy Award for Best Supporting Actor for The Wolf of Wall Street, forPerformerGender, male actor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: forPerformerGender Context triple: [Academy Award for Best Supporting Actor for The Wolf of Wall Street, forPerformerGender, male actor]
-
A.
hasPerformerGender
chosen
Indicates that an action, event, or performance is associated with the gender of the performer who carries it out.
-
B.
hasPerformerGenderComposition
Indicates the gender makeup of the group of performers involved in an event or performance.
-
C.
featuredGender
Indicates that a particular gender is highlighted, emphasized, or given primary focus in a given context or presentation.
-
D.
namedForGender
Indicates that one entity is named in a way that reflects or is derived from a particular gender or gender-related characteristic of another entity.
-
E.
genderOfPersona
Indicates the gender identity associated with a given persona.
- 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_69e29543f40c819087700b7a272afb60 |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f1d2bba58c8190a1a4b5bcc5bc9d98 |
completed | April 29, 2026, 9:43 a.m. |
| PD | Predicate disambiguation | batch_69f161578d54819084a8b35496299993 |
completed | April 29, 2026, 1:39 a.m. |
Created at: April 17, 2026, 9:26 p.m.