Triple
T7774821
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paisas |
E179164
|
entity |
| Predicate | majorCity |
P316
|
FINISHED |
| Object | Pereira |
E205447
|
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: Pereira | Statement: [Paisas, majorCity, Pereira]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pereira Context triple: [Paisas, majorCity, Pereira]
-
A.
Pereira
chosen
Pereira is a major Colombian city known as the capital of the Risaralda department and an important economic and cultural center in the country's coffee-growing region.
-
B.
Manizales
Manizales is a mountainous Colombian city known for its coffee production, cool climate, and location in the central Andes.
-
C.
Tunja
Tunja is a historic city in central Colombia known for its well-preserved colonial architecture and cultural heritage.
-
D.
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.
-
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_69c69f30602c819082ab52cd4af5c592 |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c7046331e0819080ec1a5c23c27cd7 |
completed | March 27, 2026, 10:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c96c14aa588190a660de4e356a6b07 |
completed | March 29, 2026, 6:14 p.m. |
Created at: March 27, 2026, 4:11 p.m.