Triple
T20231458
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pekár |
E495535
|
entity |
| Predicate | hasTypicalGenderForBearers |
P34349
|
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: [Pekár, hasTypicalGenderForBearers, male]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypicalGenderForBearers Context triple: [Pekár, hasTypicalGenderForBearers, male]
-
A.
hasGenderOfPerson
Indicates that a person is associated with a specific gender classification.
-
B.
hasTypicalGenderAssociation
chosen
Indicates that one entity is commonly or culturally associated with a particular gender more than with other genders.
-
C.
hasGenderInterpretation
Indicates that an entity is associated with a particular interpretation or understanding of gender.
-
D.
hasGenderRole
Indicates that an entity is associated with, or expected to perform, a particular socially defined gender-based role or set of behaviors.
-
E.
hasGenderFormat
Indicates that something is associated with or expressed in a particular gender-related format or representation.
- 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_69da626cff80819097b530718a7c98b6 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66fddafac819089cef4158f5e0ab5 |
completed | April 20, 2026, 6:26 p.m. |
| PD | Predicate disambiguation | batch_69e55b18609481909ab28bc8750a642f |
completed | April 19, 2026, 10:45 p.m. |
Created at: April 11, 2026, 11:39 p.m.