Triple
T7294304
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sorsogon Ayta language |
E164477
|
entity |
| Predicate | geoContext |
P3227
|
FINISHED |
| Object | southern part of Luzon island |
—
|
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: southern part of Luzon island | Statement: [Sorsogon Ayta language, geoContext, southern part of Luzon island]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: geoContext Context triple: [Sorsogon Ayta language, geoContext, southern part of Luzon island]
-
A.
geographicContext
chosen
Indicates that one entity is situated within, associated with, or characterized by the geographic setting or region defined by another entity.
-
B.
regionContext
Indicates the broader geographic or spatial setting within which an entity, event, or relationship is situated or interpreted.
-
C.
placesInContext
Indicates that one entity situates, interprets, or frames another entity within a particular context or surrounding circumstances.
-
D.
populationContext
Indicates the demographic or population-related setting or conditions within which an entity, event, or measurement is defined or interpreted.
-
E.
agingLocation
Indicates the place where an entity undergoes an aging process or is stored to age over time.
- 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_69c6887a499881909dd23341399c59d8 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6eb8b7cc08190983739bf667057c9 |
completed | March 27, 2026, 8:41 p.m. |
| PD | Predicate disambiguation | batch_69c6e76c5fbc8190b378830082f11cb0 |
completed | March 27, 2026, 8:24 p.m. |
Created at: March 27, 2026, 3 p.m.