Triple
T2955261
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Felipe |
E79916
|
entity |
| Predicate | nearByEconomicRegion |
P1027
|
FINISHED |
| Object | Central Chilean agricultural belt |
—
|
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: Central Chilean agricultural belt | Statement: [San Felipe, nearByEconomicRegion, Central Chilean agricultural belt]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nearByEconomicRegion Context triple: [San Felipe, nearByEconomicRegion, Central Chilean agricultural belt]
-
A.
regionalEconomyType
Indicates the type or classification of an economy associated with a specific region.
-
B.
hasMajorEconomicRegion
chosen
Indicates that an entity includes, is associated with, or is part of a primary or significant economic region within a larger economic or geographic context.
-
C.
neighboringRegion
Indicates that two regions share a common boundary or are directly adjacent to each other geographically.
-
D.
economicImpactRegion
Indicates the region or geographic area that experiences or is affected by a particular economic impact.
-
E.
nearbyUrbanCenter
Indicates that one location is geographically close to an urban center, such as a city or large town.
- 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_69ad8b1276588190a374a0b12e0f7bdf |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad99286ac8819084f02fbb0a1616d3 |
completed | March 8, 2026, 3:43 p.m. |
| PD | Predicate disambiguation | batch_69ad960c5c8881909d679912bd7d78f3 |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 2:57 p.m.