Triple
T7686243
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Melgar |
E174122
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Bogotá |
E1526
|
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: Bogotá | Statement: [Melgar, near, Bogotá]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bogotá Context triple: [Melgar, near, Bogotá]
-
A.
Bogotá
chosen
Bogotá is the high-altitude capital and largest city of Colombia, known as a major political, economic, and cultural center in South America.
-
B.
Bogotá and Medellín
Bogotá and Medellín are Colombia’s two largest and most important cities, serving as major centers of politics, culture, and commerce in the country.
-
C.
Cali
Cali is a major city in southwestern Colombia known as an important economic center and the country’s capital of salsa.
-
D.
Medellín
Medellín is Colombia’s second-largest city, known for its mountainous setting, innovative urban development, and vibrant cultural life.
-
E.
Bucaramanga
Bucaramanga is a major city in northeastern Colombia known for its mountainous setting, pleasant climate, and role as an important commercial and industrial center.
- 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_69c6995840408190a19de6c51090f46f |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c7022352088190b2510d5fac55864f |
completed | March 27, 2026, 10:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8c7b042c8819098e345be61bbe943 |
completed | March 29, 2026, 6:33 a.m. |
Created at: March 27, 2026, 4:02 p.m.