Triple
T26814215
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tuluá |
E672079
|
entity |
| Predicate | regionalEconomicImportance |
P180485
|
FINISHED |
| Object | important secondary city in Valle del Cauca |
—
|
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: important secondary city in Valle del Cauca | Statement: [Tuluá, regionalEconomicImportance, important secondary city in Valle del Cauca]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regionalEconomicImportance Context triple: [Tuluá, regionalEconomicImportance, important secondary city in Valle del Cauca]
-
A.
regionalEconomyActivity
Indicates the type or level of economic activity occurring within a specific geographic region.
-
B.
regionalEconomyType
Indicates the type or classification of an economy associated with a specific region.
-
C.
economicImpactRegion
Indicates the region or geographic area that experiences or is affected by a particular economic impact.
-
D.
economicArea
Indicates that one entity is part of, associated with, or falls under the jurisdiction of a defined economic region or zone of another entity.
-
E.
portImportance
Indicates the relative significance or strategic value of a port within a given context (such as trade, logistics, or transportation networks).
- F. None of above. chosen
Provenance (4 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_69eeb3225a3c8190aaf6746efeded2f3 |
completed | April 27, 2026, 12:51 a.m. |
| NER | Named-entity recognition | batch_69f7431c0eec81909ead443e07d75e18 |
completed | May 3, 2026, 12:44 p.m. |
| PD | Predicate disambiguation | batch_69f74143cf708190a12d487884298437 |
completed | May 3, 2026, 12:36 p.m. |
| PDg | Predicate description generation | batch_69f7431aac148190bb6aac59817c174a |
completed | May 3, 2026, 12:44 p.m. |
Created at: April 27, 2026, 4:31 a.m.