Triple
T29132515
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Belle Haleine: Eau de Voilette |
E738417
|
entity |
| Predicate | hasFemaleAlterEgoSignature |
P86336
|
FINISHED |
| Object | Rrose Sélavy |
—
|
NE NERFINISHED |
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: Rrose Sélavy | Statement: [Belle Haleine: Eau de Voilette, hasFemaleAlterEgoSignature, Rrose Sélavy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFemaleAlterEgoSignature Context triple: [Belle Haleine: Eau de Voilette, hasFemaleAlterEgoSignature, Rrose Sélavy]
-
A.
hasFemaleEquivalent
Indicates that one entity serves as the female counterpart or equivalent of another entity.
-
B.
hasFictionalAlterEgoOf
chosen
Indicates that one entity is the fictional alter ego, persona, or alternate identity of another entity.
-
C.
hasFictionalAlias
Indicates that an entity is known by an alternative name or identity within a fictional context.
-
D.
hasCrossDressingProtagonist
Indicates that the main character in the work regularly dresses in clothing traditionally associated with another gender.
-
E.
isAlsoSignedAs
Indicates that an entity is known or recorded under an additional signature, alias, or signing name besides its primary one.
- 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_69f07cb3adb48190a9e0e169cd026634 |
completed | April 28, 2026, 9:24 a.m. |
| NER | Named-entity recognition | batch_69f6c49627908190b3553474c7c3072b |
completed | May 3, 2026, 3:44 a.m. |
| PD | Predicate disambiguation | batch_69f6c3f23ae081909a52801266063a3c |
completed | May 3, 2026, 3:41 a.m. |
Created at: April 28, 2026, 11:32 a.m.