Triple
T24168454
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sans contrefaçon |
E599059
|
entity |
| Predicate | hasAndrogynousThemes |
P45364
|
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: [Sans contrefaçon, hasAndrogynousThemes, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAndrogynousThemes Context triple: [Sans contrefaçon, hasAndrogynousThemes, true]
-
A.
includesBothGenders
Indicates that the referenced group, set, or category contains members of both male and female genders.
-
B.
hasPersonalThemes
Indicates that something (such as a work, message, or expression) involves themes that are personal, intimate, or directly related to an individual’s own experiences or inner life.
-
C.
hasLGBTTheme
chosen
Indicates that the subject includes, features, or centrally involves lesbian, gay, bisexual, or transgender themes or issues.
-
D.
hasNumberOfGenders
Indicates the relationship that specifies how many distinct genders are associated with or recognized for a given entity.
-
E.
hasCrossDressingProtagonist
Indicates that the main character in the work regularly dresses in clothing traditionally associated with another gender.
- 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_69e288cbd62881909de32ca64a70c17b |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f27c9ddfcc819096697a844b300cce |
completed | April 29, 2026, 9:48 p.m. |
| PD | Predicate disambiguation | batch_69f1c42f942c8190b103ff29a60fef34 |
completed | April 29, 2026, 8:41 a.m. |
Created at: April 17, 2026, 11:33 p.m.