Triple
T6555327
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Colbún Dam |
E152432
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object | Colbún |
E441403
|
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: Colbún | Statement: [Colbún Dam, namedAfter, Colbún]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Colbún Context triple: [Colbún Dam, namedAfter, Colbún]
-
A.
Colbún
chosen
Colbún is a Chilean town and municipality in the Maule Region, known for its nearby Colbún Lake and hydroelectric facilities.
-
B.
Pasochoa
Pasochoa is an extinct volcanic mountain in Ecuador known for its lush cloud forests and rich biodiversity within a protected ecological reserve.
-
C.
Nobsa
Nobsa is a Colombian town known for its traditional wool textiles and crafts, located in the Boyacá Department.
-
D.
Horcón
Horcón is a small rural village in Chile’s Elqui Valley, known for its scenic Andean surroundings and traditional agricultural lifestyle.
-
E.
Hunza
Hunza is a mountainous valley and popular tourist destination in northern Pakistan, renowned for its dramatic Karakoram scenery and traditionally long-lived local population.
- 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_69c688058d6881908c19b309cc55dbfa |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6ae1c07cc819089c297edad943a57 |
completed | March 27, 2026, 4:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6d55707d081908104f08e1d59d603 |
completed | March 27, 2026, 7:07 p.m. |
Created at: March 27, 2026, 1:51 p.m.