Triple
T32998375
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Love's Cure, or The Martial Maid |
E844290
|
entity |
| Predicate | hasFemaleProtagonistInMaleDisguise |
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: [Love's Cure, or The Martial Maid, hasFemaleProtagonistInMaleDisguise, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFemaleProtagonistInMaleDisguise Context triple: [Love's Cure, or The Martial Maid, hasFemaleProtagonistInMaleDisguise, true]
-
A.
hasCrossDressingProtagonist
chosen
Indicates that the main character in the work regularly dresses in clothing traditionally associated with another gender.
-
B.
hasFemaleCharacter
Indicates that an entity includes or features at least one female character.
-
C.
protagonistGenderSelectable
Indicates that the gender of the story’s main character can be chosen or customized by the player or user.
-
D.
hasMaleProtagonist
Indicates that the primary main character in the work is male.
-
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_69f3494d99988190b502c68926af2c4d |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f78c61ed4c8190ad84c918fa9af55a |
completed | May 3, 2026, 5:56 p.m. |
| PD | Predicate disambiguation | batch_69f78b8cb3a881909ebaac1b503988c2 |
completed | May 3, 2026, 5:53 p.m. |
Created at: May 1, 2026, 1:22 a.m.