Triple
T37201
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Nance Garner |
E735
|
entity |
| Predicate | officeNumber |
P2996
|
FINISHED |
| Object | 32nd 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: 32nd Vice President of the United States | Statement: [John Nance Garner, officeNumber, 32nd Vice President of the United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: officeNumber Context triple: [John Nance Garner, officeNumber, 32nd Vice President of the United States]
-
A.
officeInvolved
Indicates that a particular office or organizational unit is involved or participates in a specified event, action, or relationship.
-
B.
serviceNumber
Indicates a unique identifying number assigned to a service, used to reference, track, or distinguish that service from others.
-
C.
appointmentMethod
Indicates how an appointment is arranged, such as the channel, process, or means used to schedule it.
-
D.
officeStart
Indicates the time or date at which an entity’s term, role, or period of holding office begins.
-
E.
leftOffice
Indicates that an entity ceased holding or performing the duties of a particular office or position.
- F. None of above. chosen
Provenance (4 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_69a247a8f6c08190bac804906d62ed5a |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a24bb753f081909cd8b25cfb8e08af |
completed | Feb. 28, 2026, 1:58 a.m. |
| PD | Predicate disambiguation | batch_69a24ab4a6908190b6f355415ffe7948 |
completed | Feb. 28, 2026, 1:53 a.m. |
| PDg | Predicate description generation | batch_69a24bb6881081909e7d650f2b3169d3 |
completed | Feb. 28, 2026, 1:58 a.m. |
Created at: Feb. 28, 2026, 1:46 a.m.