Triple
T32433693
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beverly |
E828797
|
entity |
| Predicate | currentPrimaryGenderUsage |
P15656
|
FINISHED |
| Object | feminine given name |
—
|
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: feminine given name | Statement: [Beverly, currentPrimaryGenderUsage, feminine given name]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: currentPrimaryGenderUsage Context triple: [Beverly, currentPrimaryGenderUsage, feminine given name]
-
A.
genderUsage
chosen
Indicates how a particular gender is applied, referenced, or treated within a given context or system.
-
B.
genderRatio
Indicates the proportional relationship between different genders within a given group or population.
-
C.
usedByGender
Indicates that something is utilized, applied, or engaged in by entities of a specified gender.
-
D.
hasAlternativeGenderUsage
Indicates that an entity is used with a different or non-standard gender form in certain contexts or usages.
-
E.
genderSpecificity
Indicates whether the relationship or action applies specifically to a particular gender or is gender-neutral.
- 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_69f3491bf298819097b610f772d54a6d |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69feba0f09508190b3e871c62b19ec7f |
completed | May 9, 2026, 4:37 a.m. |
| PD | Predicate disambiguation | batch_69feb957fe7c8190969fb31a6d1a59c8 |
completed | May 9, 2026, 4:34 a.m. |
Created at: May 1, 2026, 12:55 a.m.