Triple
T19444185
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bolivian Andes |
E486428
|
entity |
| Predicate | majorCityInRegion |
P3940
|
FINISHED |
| Object | Oruro |
—
|
NE NERFINISHED |
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: Oruro | Statement: [Bolivian Andes, majorCityInRegion, Oruro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oruro Context triple: [Bolivian Andes, majorCityInRegion, Oruro]
-
A.
Oruro
chosen
Oruro is a city in western Bolivia best known for its rich mining history and its UNESCO-recognized Carnival, one of South America's most famous folkloric festivals.
-
B.
Caranavi
Caranavi is a Bolivian town known as a key coffee-growing and agricultural hub in the Yungas region.
-
C.
San Simón
San Simón is a small municipality located in the Morazán Department of northeastern El Salvador, known for its rural character and mountainous surroundings.
-
D.
Candanchú
Candanchú is a historic ski resort in the Spanish Pyrenees, known for its alpine terrain and proximity to the French border.
-
E.
Saña
Saña is a historic town in northern Peru known for its colonial heritage and association with early Spanish ecclesiastical figures.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8d7ad488190a3373045029b0f3b |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e63387e2048190bfb13fea434ddb46 |
completed | April 20, 2026, 2:09 p.m. |
Created at: April 10, 2026, 1:38 p.m.