Triple
T22849436
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nkhum dialect |
E566315
|
entity |
| Predicate | neighboringAreas |
P141258
|
FINISHED |
| Object | border regions near China |
—
|
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: border regions near China | Statement: [Nkhum dialect, neighboringAreas, border regions near China]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: neighboringAreas Context triple: [Nkhum dialect, neighboringAreas, border regions near China]
-
A.
neighboringRegion
Indicates that two regions share a common boundary or are directly adjacent to each other geographically.
-
B.
hasNearbyCityArea
Indicates that one area is geographically close to or adjacent to a city area.
-
C.
hasNearbyGeographicalArea
chosen
Indicates that one geographical area is located in close spatial proximity to another geographical area.
-
D.
neighboringTo
Indicates that one entity is located directly adjacent or very close to another entity, sharing a common boundary or immediate vicinity.
-
E.
regionalNeighbor
Indicates that two regions share a common boundary or are directly adjacent to each other geographically.
- 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_69e2458750b481908a8e4cf4609cc6cf |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17eb74700819090d191b3a7a17034 |
completed | April 29, 2026, 3:44 a.m. |
| PD | Predicate disambiguation | batch_69eed2d507c08190895ed971af0fc755 |
completed | April 27, 2026, 3:07 a.m. |
Created at: April 17, 2026, 3:36 p.m.