Triple
T35924995
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | First Lady of Illinois |
E1038994
|
entity |
| Predicate | genderOfTypicalOfficeholder |
P34342
|
FINISHED |
| Object | 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: female | Statement: [First Lady of Illinois, genderOfTypicalOfficeholder, female]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genderOfTypicalOfficeholder Context triple: [First Lady of Illinois, genderOfTypicalOfficeholder, female]
-
A.
genderOfMostOfficeHolders
Indicates the predominant gender among individuals who hold most of the offices or positions within a given group or organization.
-
B.
genderOfTypicalHolder
chosen
Indicates the gender that is most commonly associated with or typical of the usual holder of something.
-
C.
incumbentGender
Indicates the gender of the person currently holding a particular position or office.
-
D.
genderOfPersona
Indicates the gender identity associated with a given persona.
-
E.
hasGenderOfPerson
Indicates that a person is associated with a specific gender classification.
- 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_69f76e2320748190b7f5c4750d0cd0d3 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7b69b333081909cadbed3fcb8ecf5 |
completed | May 3, 2026, 8:56 p.m. |
| PD | Predicate disambiguation | batch_69f7b4c2a5f8819094ad4621d7b97e0c |
completed | May 3, 2026, 8:49 p.m. |
Created at: May 3, 2026, 4:07 p.m.