Triple
T6565252
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kamayo language |
E153886
|
entity |
| Predicate | belongsToMacroarea |
P12634
|
FINISHED |
| Object | Papunesia |
—
|
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: Papunesia | Statement: [Kamayo language, belongsToMacroarea, Papunesia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: belongsToMacroarea Context triple: [Kamayo language, belongsToMacroarea, Papunesia]
-
A.
hasEthnologueMacroArea
Indicates that something belongs to, or is classified within, a specific macro-geographical area as defined by Ethnologue.
-
B.
arealRelation
Indicates a spatial relationship between areas, such as overlap, containment, adjacency, or relative positioning between two regions.
-
C.
macroArea
chosen
Indicates a broad geographic or regional grouping within which an entity (such as a language or location) is situated.
-
D.
hasTerritorialAssociation
Indicates a relationship where an entity is linked or connected to a specific territory, area, or geographic region.
-
E.
regionCorrespondsRoughlyTo
Indicates that one region approximately matches or aligns with another in location, extent, or boundaries, but not with precise or exact correspondence.
- 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_69c6880cb35881909b763eb0125236b9 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6cc9c6cb0819084fec8e0beb430de |
completed | March 27, 2026, 6:29 p.m. |
| PD | Predicate disambiguation | batch_69c6acf93cb48190b54f5dd6febd34dc |
completed | March 27, 2026, 4:14 p.m. |
Created at: March 27, 2026, 1:52 p.m.