Triple
T35583177
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | First Lady of Wisconsin |
E1028274
|
entity |
| Predicate | canBeMaleEquivalent |
P15994
|
FINISHED |
| Object | First Gentleman of Wisconsin |
—
|
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: First Gentleman of Wisconsin | Statement: [First Lady of Wisconsin, canBeMaleEquivalent, First Gentleman of Wisconsin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: canBeMaleEquivalent Context triple: [First Lady of Wisconsin, canBeMaleEquivalent, First Gentleman of Wisconsin]
-
A.
hasFemaleEquivalent
Indicates that one entity serves as the female counterpart or equivalent of another entity.
-
B.
maleEquivalent
chosen
Indicates that one entity is the corresponding male counterpart or equivalent of another entity.
-
C.
canBeMaleName
Indicates that something is a possible or valid given name for a male.
-
D.
hasGenderVariant
Indicates that one entity is a gender-specific form or variant of another entity.
-
E.
bearerGender
Indicates the gender associated with the bearer in the relationship or context.
- 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_69f76e0495a081909beced418558c0b4 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fed6da0390819096b88ef4714b144e |
completed | May 9, 2026, 6:40 a.m. |
| PD | Predicate disambiguation | batch_69fed53517d081909966f31707625f1a |
completed | May 9, 2026, 6:33 a.m. |
Created at: May 3, 2026, 4:04 p.m.