Triple
T31498957
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hojagiri |
E803622
|
entity |
| Predicate | typicalPerformersGender |
P81870
|
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: [Hojagiri, typicalPerformersGender, female]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalPerformersGender Context triple: [Hojagiri, typicalPerformersGender, female]
-
A.
hasPerformerGender
Indicates that an action, event, or performance is associated with the gender of the performer who carries it out.
-
B.
originalPerformerGender
Indicates the gender of the person who first performed or originated the work, role, or piece.
-
C.
hasPerformerGenderComposition
chosen
Indicates the gender makeup of the group of performers involved in an event or performance.
-
D.
typicalPerformers
Indicates the entities that most commonly or characteristically perform a given action or role.
-
E.
typicalPerformerCount
Indicates the usual or standard number of performers involved in carrying out the associated activity or event.
- 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_69f348cae52081909fa8e5f697523ae3 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_6a037c876524819098545e6037d3107d |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379e7aa0c8190bdc9ee4d54fc821b |
completed | May 12, 2026, 7:05 p.m. |
Created at: April 30, 2026, 9:43 p.m.