Triple
T28259964
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Les Six |
E712556
|
entity |
| Predicate | hasGenderMix |
P136236
|
FINISHED |
| Object | mixed-gender group |
—
|
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: mixed-gender group | Statement: [Les Six, hasGenderMix, mixed-gender group]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGenderMix Context triple: [Les Six, hasGenderMix, mixed-gender group]
-
A.
hasNumberOfGenders
Indicates the relationship that specifies how many distinct genders are associated with or recognized for a given entity.
-
B.
hasGenderVariant
Indicates that one entity is a gender-specific form or variant of another entity.
-
C.
hasGenderSystem
Indicates that an entity employs or is characterized by a particular system for categorizing gender.
-
D.
hasGenderNeutrality
Indicates that something (such as a term, form, or expression) is neutral with respect to gender and does not specify or imply any particular gender.
-
E.
includesBothGenders
chosen
Indicates that the referenced group, set, or category contains members of both male and female genders.
- 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_69efb5207eb08190827e4c34048030b1 |
completed | April 27, 2026, 7:12 p.m. |
| NER | Named-entity recognition | batch_69f727afd5d88190ad48735cd1b32787 |
completed | May 3, 2026, 10:47 a.m. |
| PD | Predicate disambiguation | batch_69f72737c42c8190a3f781a5e98868ff |
completed | May 3, 2026, 10:45 a.m. |
Created at: April 27, 2026, 11:11 p.m.