Triple
T10997369
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | William Hartnett |
E259912
|
entity |
| Predicate | possibleGender |
P39348
|
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: [William Hartnett, possibleGender, male]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: possibleGender Context triple: [William Hartnett, possibleGender, male]
-
A.
hasNumberOfGenders
Indicates the relationship that specifies how many distinct genders are associated with or recognized for a given entity.
-
B.
hasGenderRole
Indicates that an entity is associated with, or expected to perform, a particular socially defined gender-based role or set of behaviors.
-
C.
hasGenderVariant
Indicates that one entity is a gender-specific form or variant of another entity.
-
D.
hasGenderOfPerson
chosen
Indicates that a person is associated with a specific gender classification.
-
E.
genderConfiguration
Indicates how the genders of the involved entities are arranged or combined within a particular relationship or context.
- 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_69d6aa8a6a548190a750f944ccdc8064 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d796d20b448190958331705de3a9be |
completed | April 9, 2026, 12:08 p.m. |
| PD | Predicate disambiguation | batch_69d72e93ac648190b46c5d12bf3eb1e9 |
completed | April 9, 2026, 4:44 a.m. |
Created at: April 8, 2026, 9:24 p.m.