Triple
T37078392
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | St. Luke Penny Savings Bank |
E917774
|
entity |
| Predicate | genderOfFoundingLeader |
P51361
|
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: [St. Luke Penny Savings Bank, genderOfFoundingLeader, female]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genderOfFoundingLeader Context triple: [St. Luke Penny Savings Bank, genderOfFoundingLeader, female]
-
A.
founderGender
chosen
Indicates the gender identity of the person who founded an organization, company, or entity.
-
B.
genderOfEponym
Indicates the gender of the person after whom something (such as a place, object, or concept) is named.
-
C.
hasFemaleLeader
Indicates that the subject entity is led or governed by a woman in a primary leadership role.
-
D.
genderOfMostOfficeHolders
Indicates the predominant gender among individuals who hold most of the offices or positions within a given group or organization.
-
E.
honoreeGender
Indicates the gender associated with the person who is being honored in the given 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_69f76e9771e08190a690834e3cd20654 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fb34e5576881909394355c8ec6ddd2 |
completed | May 6, 2026, 12:32 p.m. |
| PD | Predicate disambiguation | batch_69fb2f6171e88190bf1e0ee6a644b6a9 |
completed | May 6, 2026, 12:09 p.m. |
Created at: May 3, 2026, 4:14 p.m.