Triple
T26326337
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Western Boyacá Province |
E662261
|
entity |
| Predicate | hasRegionalEconomySector |
P47145
|
FINISHED |
| Object | agriculture |
—
|
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: agriculture | Statement: [Western Boyacá Province, hasRegionalEconomySector, agriculture]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRegionalEconomySector Context triple: [Western Boyacá Province, hasRegionalEconomySector, agriculture]
-
A.
regionalEconomyType
Indicates the type or classification of an economy associated with a specific region.
-
B.
associatedWithEconomicSector
chosen
Indicates that an entity has a connection or involvement with a particular economic sector, such as operating, participating, or being relevant within that sector.
-
C.
regionalEconomyActivity
Indicates the type or level of economic activity occurring within a specific geographic region.
-
D.
hasIndustrialSector
Indicates that an entity is associated with, operates in, or belongs to a particular industrial sector or branch of economic activity.
-
E.
hasMajorEconomicRegion
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.
- 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_69ee812f32748190871d970c4e2a8ddf |
completed | April 26, 2026, 9:18 p.m. |
| NER | Named-entity recognition | batch_69fd884cb2b48190b6acd473430d9e19 |
completed | May 8, 2026, 6:53 a.m. |
| PD | Predicate disambiguation | batch_69fd8709ca208190a8bab836f0156af5 |
completed | May 8, 2026, 6:47 a.m. |
Created at: April 26, 2026, 10:31 p.m.