Triple
T6594051
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Waimate |
E148431
|
entity |
| Predicate | hasRegionSeatRole |
P12921
|
FINISHED |
| Object | service town for surrounding rural area |
—
|
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: service town for surrounding rural area | Statement: [Waimate, hasRegionSeatRole, service town for surrounding rural area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRegionSeatRole Context triple: [Waimate, hasRegionSeatRole, service town for surrounding rural area]
-
A.
hasRegionalRole
chosen
Indicates that an entity holds a specific role, function, or responsibility within a defined geographic region.
-
B.
regionSeatLocatedIn
Indicates that the administrative seat or capital of a region is located within that region.
-
C.
hasRegion
Indicates that an entity includes, contains, or is associated with a specific geographic or administrative region as part of its scope or structure.
-
D.
isRegionOf
Indicates that one entity is a geographic or administrative region belonging to, contained within, or associated with another entity.
-
E.
hasDepartmentSeatRole
Indicates that an entity holds a specific role or position associated with a seat in a particular department.
- 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_69c687e7b8688190811ffee72e096468 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6cc9c6cb0819084fec8e0beb430de |
completed | March 27, 2026, 6:29 p.m. |
| PD | Predicate disambiguation | batch_69c6acfd17388190bd0bb8b2371e7df1 |
completed | March 27, 2026, 4:14 p.m. |
Created at: March 27, 2026, 1:55 p.m.