Triple
T10191261
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charles-François |
E238038
|
entity |
| Predicate | nameGenderUsage |
P81607
|
FINISHED |
| Object | primarily male |
—
|
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: primarily male | Statement: [Charles-François, nameGenderUsage, primarily male]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nameGenderUsage Context triple: [Charles-François, nameGenderUsage, primarily male]
-
A.
hasNameGenderUsage
chosen
Indicates that a particular name is used with a specific gender or set of genders in a given context.
-
B.
genderUsage
Indicates how a particular gender is applied, referenced, or treated within a given context or system.
-
C.
namedForGender
Indicates that one entity is named in a way that reflects or is derived from a particular gender or gender-related characteristic of another entity.
-
D.
genderSignificance
Indicates the relevance or impact that an entity’s gender has within a particular context, relationship, or interpretation.
-
E.
usedByGender
Indicates that something is utilized, applied, or engaged in by entities of a specified 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_69ca84de1b208190bf17bb305b002605 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cded7eb1148190a2d175163685e233 |
completed | April 2, 2026, 4:15 a.m. |
| PD | Predicate disambiguation | batch_69cd7c8477648190bc55c56aeec507d3 |
completed | April 1, 2026, 8:13 p.m. |
Created at: March 30, 2026, 9:13 p.m.