Triple
T15286239
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mesa Grande Diegueño |
E365406
|
entity |
| Predicate | geographicProximityTo |
P55639
|
FINISHED |
| Object | Barona Diegueño dialect |
—
|
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: Barona Diegueño dialect | Statement: [Mesa Grande Diegueño, geographicProximityTo, Barona Diegueño dialect]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: geographicProximityTo Context triple: [Mesa Grande Diegueño, geographicProximityTo, Barona Diegueño dialect]
-
A.
nearbyTo
Indicates that one entity is located close in distance or position to another entity.
-
B.
featuresRegionalProximity
chosen
Indicates that one entity is located near or in close geographic proximity to a particular region or another entity.
-
C.
proximityToLandmark
Indicates a spatial relationship where one entity is located near or close to a specified landmark.
-
D.
residesNear
Indicates that one entity lives or is located in close physical proximity to another entity.
-
E.
nearbyLocation
Indicates that one location is situated close to another location in physical space.
- 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_69d85a103d9081908c1ea6c4c73ac8e3 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e00e551bb0819094db097285443740 |
completed | April 15, 2026, 10:16 p.m. |
| PD | Predicate disambiguation | batch_69deca90739081909bd1b797cdb8af2b |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:15 a.m.