Triple
T9529605
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Outstanding Supporting Actor in a Drama Series |
E229851
|
entity |
| Predicate | isGenderedCategory |
P55024
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Outstanding Supporting Actor in a Drama Series, isGenderedCategory, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isGenderedCategory Context triple: [Outstanding Supporting Actor in a Drama Series, isGenderedCategory, true]
-
A.
isGenderSpecificCategory
chosen
Indicates that the category applies specifically to one gender rather than being gender-neutral.
-
B.
isUnisex
Indicates that something is suitable, designed, or intended for use by individuals of any gender.
-
C.
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.
-
D.
playsGender
Indicates that one entity performs or assumes a particular gender role or identity in a given context.
-
E.
genderCategories
Indicates the classification of an entity into one or more gender-related categories or identities.
- 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_69ca8479934c81908006d0e6e970ae05 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd98b1b93481909812245ac14e4988 |
completed | April 1, 2026, 10:14 p.m. |
| PD | Predicate disambiguation | batch_69cca56c44f88190a54a5d2a133bb07e |
completed | April 1, 2026, 4:56 a.m. |
Created at: March 30, 2026, 8 p.m.