Triple
T6825376
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Governor of Michigan |
E157000
|
entity |
| Predicate | equivalentOfficeIn |
P15578
|
FINISHED |
| Object | Governor of California |
—
|
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: Governor of California | Statement: [Governor of Michigan, equivalentOfficeIn, Governor of California]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: equivalentOfficeIn Context triple: [Governor of Michigan, equivalentOfficeIn, Governor of California]
-
A.
equivalentOffice
chosen
Indicates that two offices are considered functionally or formally the same position, role, or authority, even if they differ in name or jurisdiction.
-
B.
comparableOffice
Indicates that two offices are sufficiently similar in relevant characteristics (such as size, function, or status) to be meaningfully compared to each other.
-
C.
includedOffice
Indicates that one office is contained within, or forms part of, another office or organizational unit.
-
D.
relatesToOffice
Indicates that one entity has a connection, association, or relevance to an office, its functions, or its environment.
-
E.
specifiesOffice
Indicates that an entity is assigned to or associated with a particular office or official position.
- 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_69c6882a5b5c8190917a7db9ed36bad1 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d581ea5881908ba78c6bf1ce58ee |
completed | March 27, 2026, 7:07 p.m. |
| PD | Predicate disambiguation | batch_69c6d09bb4f881909bf20c188cb3e8e1 |
completed | March 27, 2026, 6:46 p.m. |
Created at: March 27, 2026, 2:18 p.m.