Triple
T663446
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Margaret River |
E12806
|
entity |
| Predicate | hasSubregionStatus |
P18079
|
FINISHED |
| Object | Geographical Indication |
—
|
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: Geographical Indication | Statement: [Margaret River, hasSubregionStatus, Geographical Indication]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSubregionStatus Context triple: [Margaret River, hasSubregionStatus, Geographical Indication]
-
A.
hasSubdivision
Indicates that one entity is divided into and contains another entity as one of its constituent parts or administrative units.
-
B.
hasRegion
Indicates that an entity includes, contains, or is associated with a specific geographic or administrative region as part of its scope or structure.
-
C.
hasSubnationalCounterpart
Indicates that an entity has a corresponding or equivalent entity at a lower (subnational) administrative level.
-
D.
subregionOf
Indicates that one region is geographically or administratively contained within, and is a part of, another larger region.
-
E.
hasRegionalRole
Indicates that an entity holds a specific role, function, or responsibility within a defined geographic region.
- 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_69a493355dec819098d4244b2fa34885 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49fd1f0ec819087003d30bbab2fa6 |
completed | March 1, 2026, 8:21 p.m. |
| PD | Predicate disambiguation | batch_69a49d153a948190b3ccdc331ed33617 |
completed | March 1, 2026, 8:09 p.m. |
| PDg | Predicate description generation | batch_69a49ee356c0819085e2e82831cf1360 |
completed | March 1, 2026, 8:17 p.m. |
Created at: March 1, 2026, 7:36 p.m.