Triple
T1941700
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Suesca |
E41567
|
entity |
| Predicate | hasNearbyTown |
P3883
|
FINISHED |
| Object | Gachancipá |
E227047
|
NE 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: Gachancipá | Statement: [Suesca, hasNearbyTown, Gachancipá]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gachancipá Context triple: [Suesca, hasNearbyTown, Gachancipá]
-
A.
Gachancipá
chosen
Gachancipá is a municipality in the Cundinamarca Department of Colombia, located in the central highlands near Bogotá.
-
B.
Sibaté
Sibaté is a municipality in central Colombia known for its agricultural production and proximity to Bogotá within the Cundinamarca Department.
-
C.
Comayagüela
Comayagüela is a major urban district of Honduras that, together with Tegucigalpa, forms the country’s capital area.
-
D.
Tocancipá
Tocancipá is a Colombian municipality in the department of Cundinamarca, known for its industrial activity, motorsport circuit, and proximity to Bogotá.
-
E.
Pitalito
Pitalito is a major town and coffee-producing hub in southern Colombia, known as one of the country’s most important centers for high-quality coffee.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69a88649b24c819080047f26b6db2ded |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb2fc98e881909a539c0ebf842d8b |
completed | March 7, 2026, 5:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae26feafb08190a4e7152ec2b4fcba |
completed | March 9, 2026, 1:48 a.m. |
Created at: March 4, 2026, 7:36 p.m.