Triple
T1563863
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Santander Department |
E33387
|
entity |
| Predicate | largestCity |
P235
|
FINISHED |
| Object | Bucaramanga |
E187310
|
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: Bucaramanga | Statement: [Santander Department, largestCity, Bucaramanga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bucaramanga Context triple: [Santander Department, largestCity, Bucaramanga]
-
A.
Bucaramanga
chosen
Bucaramanga is a major city in northeastern Colombia known for its mountainous setting, pleasant climate, and role as an important commercial and industrial center.
-
B.
Apartadó
Apartadó is a municipality in Colombia’s Antioquia Department, known as an important agricultural and commercial center in the Urabá region, especially for banana production.
-
C.
Tunja
Tunja is a historic city in central Colombia known for its well-preserved colonial architecture and cultural heritage.
-
D.
Manizales
Manizales is a mountainous Colombian city known for its coffee production, cool climate, and location in the central Andes.
-
E.
Medellín
Medellín is Colombia’s second-largest city, known for its mountainous setting, innovative urban development, and vibrant cultural life.
- 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_69a885ef9cf48190b0af0f5ce3d02231 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a9089c7b9881909e44fee8053ac189 |
completed | March 5, 2026, 4:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae5d78933c81908359b0010b9e6147 |
completed | March 9, 2026, 5:41 a.m. |
Created at: March 4, 2026, 7:27 p.m.