Triple
T33422518
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Outstanding Supporting Actor in a Drama Series |
E855881
|
entity |
| Predicate | isSeparatedByGender |
P49314
|
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: [Outstanding Supporting Actor in a Drama Series, isSeparatedByGender, male]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isSeparatedByGender Context triple: [Outstanding Supporting Actor in a Drama Series, isSeparatedByGender, male]
-
A.
hasGenderDivisions
chosen
Indicates that something is organized, classified, or separated into groups based on gender.
-
B.
isSingleSex
Indicates that the entity involves or is restricted to only one biological sex or gender, rather than being mixed or coeducational.
-
C.
hasGenderDistinction
Indicates that a relationship, classification, or linguistic form differentiates entities based on gender categories.
-
D.
hasGenderConvention
Indicates that there is an established or customary way of assigning or expressing gender within a given context, system, or culture.
-
E.
hasNumberOfGenders
Indicates the relationship that specifies how many distinct genders are associated with or recognized for a given entity.
- 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_69f3496fdf0081908c1aa30870ce518b |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6fb19063c81909466b329655c8583 |
completed | May 3, 2026, 7:36 a.m. |
| PD | Predicate disambiguation | batch_69f6f96badb08190994442c2aba840b1 |
completed | May 3, 2026, 7:29 a.m. |
Created at: May 1, 2026, 1:36 a.m.