Triple
T22251371
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mademoiselle de Maupin |
E549985
|
entity |
| Predicate | hasFemaleProtagonistDisguisedAsMan |
P146968
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Mademoiselle de Maupin, hasFemaleProtagonistDisguisedAsMan, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFemaleProtagonistDisguisedAsMan Context triple: [Mademoiselle de Maupin, hasFemaleProtagonistDisguisedAsMan, true]
-
A.
hasCrossDressingProtagonist
chosen
Indicates that the main character in the work regularly dresses in clothing traditionally associated with another gender.
-
B.
hasMaleProtagonist
Indicates that the primary main character in the work is male.
-
C.
protagonistGenderSelectable
Indicates that the gender of the story’s main character can be chosen or customized by the player or user.
-
D.
hasFemaleCharacter
Indicates that an entity includes or features at least one female character.
-
E.
hasFemaleEquivalent
Indicates that one entity serves as the female counterpart or equivalent of another entity.
- 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_69e11e41d9408190bd770cf282e22753 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f138befa208190877760dec1896740 |
completed | April 28, 2026, 10:46 p.m. |
| PD | Predicate disambiguation | batch_69e72fe1e0cc8190bd13cff2a0846225 |
completed | April 21, 2026, 8:05 a.m. |
Created at: April 16, 2026, 8:39 p.m.