Triple
T6353429
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kazuko |
E142931
|
entity |
| Predicate | typicalBearerGender |
P34342
|
FINISHED |
| Object | female |
—
|
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: female | Statement: [Kazuko, typicalBearerGender, female]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalBearerGender Context triple: [Kazuko, typicalBearerGender, female]
-
A.
genderOfTypicalHolder
chosen
Indicates the gender that is most commonly associated with or typical of the usual holder of something.
-
B.
hasTypicalGenderAssociation
Indicates that one entity is commonly or culturally associated with a particular gender more than with other genders.
-
C.
genderOfFirstHolder
Indicates that the relationship specifies the gender of the first entity that holds or possesses something in the described context.
-
D.
hasGenderOfPerson
Indicates that a person is associated with a specific gender classification.
-
E.
hasGenderInterpretation
Indicates that an entity is associated with a particular interpretation or understanding of gender.
- 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_69c008d6dcbc8190aa1c2f1fd8916b42 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c067dec4a88190992d57a0cc7782ad |
completed | March 22, 2026, 10:06 p.m. |
| PD | Predicate disambiguation | batch_69c060ec091c8190912aac44e1b8b1c9 |
completed | March 22, 2026, 9:36 p.m. |
Created at: March 22, 2026, 4:31 p.m.