Triple
T21451856
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lois Irene Kimsey |
E529228
|
entity |
| Predicate | hasSpousePositionOrdinal |
P26554
|
FINISHED |
| Object | 28th Vice President of the United States |
—
|
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: 28th Vice President of the United States | Statement: [Lois Irene Kimsey, hasSpousePositionOrdinal, 28th Vice President of the United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSpousePositionOrdinal Context triple: [Lois Irene Kimsey, hasSpousePositionOrdinal, 28th Vice President of the United States]
-
A.
hasSpousePositionInFamily
Indicates that a person’s spouse holds a specific role or position within the family structure.
-
B.
hasSpouseMilitaryRank
Indicates that a person’s spouse holds a specific military rank.
-
C.
spouseOffice
chosen
Indicates that one entity holds an office or position that is associated with, or held by, the spouse of another entity.
-
D.
spouseOrdinalNumberAsPresident
Indicates the numerical order in which a person’s spouse served as president (e.g., first, second, third).
-
E.
spouseLaterOffice
Indicates that one person’s spouse held a particular office or position at a later time than the person in question.
- 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_69e0c457579481909db68053ed99750c |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9e9d33e648190864b0ef5acf36659 |
completed | April 23, 2026, 9:43 a.m. |
| PD | Predicate disambiguation | batch_69e631df1b38819088d3604854e697b4 |
completed | April 20, 2026, 2:02 p.m. |
Created at: April 16, 2026, 6:07 p.m.