Triple
T344180
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aconcagua |
E6901
|
entity |
| Predicate | nearestMajorCity |
P1982
|
FINISHED |
| Object | Mendoza |
E50047
|
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: Mendoza | Statement: [Aconcagua, nearestMajorCity, Mendoza]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mendoza Context triple: [Aconcagua, nearestMajorCity, Mendoza]
-
A.
Mendoza Province
chosen
Mendoza Province is a region in western Argentina known for its Andean landscapes, including the towering Aconcagua peak, and its prominent wine-producing industry.
-
B.
Bariloche
Bariloche is a popular Argentine city in the Andean region known for its lakes, mountains, skiing, and Swiss-style alpine architecture.
-
C.
Catamarca Province
Catamarca Province is a sparsely populated, mountainous province in northwestern Argentina known for its high Andean peaks, arid landscapes, and rich mining and colonial history.
-
D.
La Calera
La Calera is a Colombian town and municipality in the Andean department of Cundinamarca, known for its mountainous landscapes and proximity to Bogotá.
-
E.
Concepción
Concepción is a major Chilean city in the south-central part of the country, known as an important industrial, commercial, and educational 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_69a2e7951ba08190960e90823b5078f3 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2eb0019088190a9b969c4287dc4fa |
completed | Feb. 28, 2026, 1:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a40ad03f5c819085d2c7686f4d2809 |
completed | March 1, 2026, 9:45 a.m. |
Created at: Feb. 28, 2026, 1:08 p.m.