Triple
T2732541
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Primetime Emmy Award for Outstanding Guest Actor in a Comedy Series |
E60347
|
entity |
| Predicate | sexOrGenderOfRecipient |
P72
|
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: [Primetime Emmy Award for Outstanding Guest Actor in a Comedy Series, sexOrGenderOfRecipient, male]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sexOrGenderOfRecipient Context triple: [Primetime Emmy Award for Outstanding Guest Actor in a Comedy Series, sexOrGenderOfRecipient, male]
-
A.
sexOrGender
chosen
Indicates that one entity has a specified biological sex or socially constructed gender identity.
-
B.
hasGenderOfRecipients
Indicates the gender category or composition of the recipients involved in a given relationship or action.
-
C.
genderRule
Indicates a rule or constraint that determines how gender-related properties or classifications should be assigned or interpreted in a given context.
-
D.
genderOfTypicalHolder
Indicates the gender that is most commonly associated with or typical of the usual holder of something.
-
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_69ab4b75cd908190b691ef0d1801acda |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdaf011548190beb9c3feee7b743f |
completed | March 7, 2026, 7:59 a.m. |
| PD | Predicate disambiguation | batch_69abd82859348190bce3be8f2e9d60ba |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:56 p.m.