Triple
T239327
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cundinamarca Department |
E4892
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | La Mesa |
E31998
|
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: La Mesa | Statement: [Cundinamarca Department, contains, La Mesa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: La Mesa Context triple: [Cundinamarca Department, contains, La Mesa]
-
A.
La Mesa
chosen
La Mesa is a municipality in the Cundinamarca Department of Colombia, known for its mild climate and agricultural production.
-
B.
Ovalle
Ovalle is a Chilean city known as an agricultural and commercial center in the north-central part of the country.
-
C.
Cáqueza
Cáqueza is a small municipality and town in the Andean region of central Colombia, known for its rural landscapes and proximity to Bogotá in the department of Cundinamarca.
-
D.
Hidalgo
Hidalgo is a central Mexican state known for its mountainous terrain, rich mining history, and diverse indigenous cultural heritage.
-
E.
Quivicán
Quivicán is a municipality in western Cuba known for its agricultural activities and location within the province surrounding Havana.
- 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_69a257c3d0708190b0871c4269d273e6 |
completed | Feb. 28, 2026, 2:49 a.m. |
| NER | Named-entity recognition | batch_69a25ceaecdc81909e9ff49cb6a4e02a |
completed | Feb. 28, 2026, 3:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3736ff4388190b6b43a149d0003c5 |
completed | Feb. 28, 2026, 11 p.m. |
Created at: Feb. 28, 2026, 2:53 a.m.