Triple
T24591166
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boston Society of Film Critics Award for Best Supporting Actor |
E608541
|
entity |
| Predicate | hasCategoryGender |
P55024
|
FINISHED |
| Object | male |
—
|
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 | Statement: [Boston Society of Film Critics Award for Best Supporting Actor, hasCategoryGender, male]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCategoryGender Context triple: [Boston Society of Film Critics Award for Best Supporting Actor, hasCategoryGender, male]
-
A.
isGenderSpecificCategory
chosen
Indicates that the category applies specifically to one gender rather than being gender-neutral.
-
B.
overseesGenderCategory
Indicates that one entity has responsibility for supervising, managing, or administering a particular gender category associated with another entity.
-
C.
genderCategories
Indicates the classification of an entity into one or more gender-related categories or identities.
-
D.
usedByGender
Indicates that something is utilized, applied, or engaged in by entities of a specified gender.
-
E.
hasGenderOfPerson
Indicates that a person is associated with a specific gender classification.
- 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_69e2c4cf54248190af7b0c2d9ade9830 |
completed | April 17, 2026, 11:39 p.m. |
| NER | Named-entity recognition | batch_69f2a9db3a9481908c7a59281e2ca994 |
completed | April 30, 2026, 1:01 a.m. |
| PD | Predicate disambiguation | batch_69f2a6c1f07081908edf0b521767e79b |
completed | April 30, 2026, 12:48 a.m. |
Created at: April 18, 2026, 2:30 a.m.