Triple
T30921295
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Notre-Dame des Anges |
E787729
|
entity |
| Predicate | hasGenderOfFigure |
P95608
|
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: [Notre-Dame des Anges, hasGenderOfFigure, female]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGenderOfFigure Context triple: [Notre-Dame des Anges, hasGenderOfFigure, female]
-
A.
hasGenderOfPerson
Indicates that a person is associated with a specific gender classification.
-
B.
genderDepicted
chosen
Indicates that the relationship specifies the gender of the entity as it is represented or portrayed in some context.
-
C.
hasGenderVariant
Indicates that one entity is a gender-specific form or variant of another entity.
-
D.
hasGenderInterpretation
Indicates that an entity is associated with a particular interpretation or understanding of gender.
-
E.
hasGenderRole
Indicates that an entity is associated with, or expected to perform, a particular socially defined gender-based role or set of behaviors.
- 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_69f224bfaca88190b9d0dfcc86297fe9 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69fb2e940d5c8190bceae77daf4ef512 |
completed | May 6, 2026, 12:05 p.m. |
| PD | Predicate disambiguation | batch_69f9fec70bd881909c658a3c5020318b |
completed | May 5, 2026, 2:29 p.m. |
Created at: April 29, 2026, 8:51 p.m.