Triple
T35842451
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vickie |
E1036120
|
entity |
| Predicate | hasAssociatedGenderUsage |
P34349
|
FINISHED |
| Object | primarily female |
—
|
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 female | Statement: [Vickie, hasAssociatedGenderUsage, primarily female]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAssociatedGenderUsage Context triple: [Vickie, hasAssociatedGenderUsage, primarily female]
-
A.
hasAlternativeGenderUsage
Indicates that an entity is used with a different or non-standard gender form in certain contexts or usages.
-
B.
hasTypicalGenderAssociation
chosen
Indicates that one entity is commonly or culturally associated with a particular gender more than with other genders.
-
C.
usedByGender
Indicates that something is utilized, applied, or engaged in by entities of a specified gender.
-
D.
hasGenderFormat
Indicates that something is associated with or expressed in a particular gender-related format or representation.
-
E.
hasGenderVariant
Indicates that one entity is a gender-specific form or variant 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_69f76e1a29e8819088280f26096aeb55 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fd474b7e788190a9bb9b542d878f60 |
completed | May 8, 2026, 2:15 a.m. |
| PD | Predicate disambiguation | batch_69fd46d8b2f0819099d92d72c902f60e |
completed | May 8, 2026, 2:13 a.m. |
Created at: May 3, 2026, 4:06 p.m.