Triple
T25929471
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Florence A. Blanchfield |
E653394
|
entity |
| Predicate | firstWomanWithRegularArmyCommission |
P4487
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Florence A. Blanchfield, firstWomanWithRegularArmyCommission, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstWomanWithRegularArmyCommission Context triple: [Florence A. Blanchfield, firstWomanWithRegularArmyCommission, true]
-
A.
receivedRegularArmyCommission
Indicates that an individual was formally granted an official commission as an officer in a country's regular (standing) army.
-
B.
firstFemaleHolderDate
Indicates the date on which the first female individual came to hold a given position, title, or role.
-
C.
isFirstFemaleHolderOfOffice
chosen
Indicates that a person is the first woman ever to hold a particular office or position.
-
D.
enabledFirstWomanMPToSit
Indicates that one entity made it possible for the first woman Member of Parliament to take her seat in the legislature.
-
E.
enabledFirstWomanMPToBeElected
Indicates that the subject’s actions or influence made it possible for the first woman Member of Parliament to be elected.
- 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_69e7ab3eb9b881909c1390690551f868 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69fd5f29b1988190877764ef2a399c7f |
completed | May 8, 2026, 3:57 a.m. |
| PD | Predicate disambiguation | batch_69fd5e30194c819085b5ce586122ab37 |
completed | May 8, 2026, 3:53 a.m. |
Created at: April 22, 2026, 8:36 a.m.