Triple
T32998376
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Love's Cure, or The Martial Maid |
E844290
|
entity |
| Predicate | hasMaleCharacterRaisedAsFemale |
P202771
|
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: [Love's Cure, or The Martial Maid, hasMaleCharacterRaisedAsFemale, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMaleCharacterRaisedAsFemale Context triple: [Love's Cure, or The Martial Maid, hasMaleCharacterRaisedAsFemale, true]
-
A.
genderReversalOf
Indicates that one entity is a counterpart of another with the same role or characteristics but with the opposite gender.
-
B.
hasFemaleCharacter
Indicates that an entity includes or features at least one female character.
-
C.
hasCrossDressingProtagonist
Indicates that the main character in the work regularly dresses in clothing traditionally associated with another 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.
hasFemaleEquivalent
Indicates that one entity serves as the female counterpart or equivalent of another entity.
- F. None of above. chosen
Provenance (4 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_69f3494d99988190b502c68926af2c4d |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a00b6e163308190adeb1d6eb8558641 |
completed | May 10, 2026, 4:48 p.m. |
| PD | Predicate disambiguation | batch_6a00b67e0e388190b08ad66cee4cb0d7 |
completed | May 10, 2026, 4:46 p.m. |
| PDg | Predicate description generation | batch_6a00b6e0b954819094e63589d718b9af |
completed | May 10, 2026, 4:48 p.m. |
Created at: May 1, 2026, 1:22 a.m.