Triple
T8043463
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Woman in a Chemise |
E187489
|
entity |
| Predicate | hasGenderOfDepictedPerson |
P39348
|
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: [Woman in a Chemise, hasGenderOfDepictedPerson, female]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGenderOfDepictedPerson Context triple: [Woman in a Chemise, hasGenderOfDepictedPerson, female]
-
A.
hasGenderOfPerson
chosen
Indicates that a person is associated with a specific gender classification.
-
B.
hasGenderInterpretation
Indicates that an entity is associated with a particular interpretation or understanding of gender.
-
C.
hasGenderRole
Indicates that an entity is associated with, or expected to perform, a particular socially defined gender-based role or set of behaviors.
-
D.
hasGenderInText
Indicates that a specified gender is explicitly mentioned or assigned to an entity within a given text.
-
E.
playsGender
Indicates that one entity performs or assumes a particular gender role or identity in a given 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_69ca82b00cb48190b59a300f70e97bd7 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3f4b5e0c819092949af0f995b850 |
completed | March 31, 2026, 3:28 a.m. |
| PD | Predicate disambiguation | batch_69cb049688208190b32088bd2c5930bc |
completed | March 30, 2026, 11:17 p.m. |
Created at: March 30, 2026, 5:23 p.m.