Triple
T32493003
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mark Tippetts |
E830440
|
entity |
| Predicate | officeTypeSought |
P9181
|
FINISHED |
| Object | state governor |
—
|
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: state governor | Statement: [Mark Tippetts, officeTypeSought, state governor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: officeTypeSought Context triple: [Mark Tippetts, officeTypeSought, state governor]
-
A.
officeSoughtIn
Indicates that an entity is seeking, running for, or otherwise pursuing a particular office or position within a specified jurisdiction or context.
-
B.
targetOfficeSought
chosen
Indicates the specific public or organizational office or position that an individual is seeking or running for.
-
C.
employmentType
Indicates the specific kind or category of employment relationship that exists between an individual and an employer (e.g., full-time, part-time, contract).
-
D.
hasOfficeType
Indicates that an entity’s office is classified as a specific type or category of office.
-
E.
occupationType
Indicates the specific kind or category of work, profession, or role that an entity performs or holds.
- 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_69f34920aa4081908d8fb0277414b911 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69ff289541e0819096eeceb8e6332650 |
completed | May 9, 2026, 12:29 p.m. |
| PD | Predicate disambiguation | batch_69ff281ab1988190920f0443be9f10cc |
completed | May 9, 2026, 12:27 p.m. |
Created at: May 1, 2026, 12:59 a.m.