Triple
T11979372
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Etcheverry |
E285117
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Echeverri |
E116872
|
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: Echeverri | Statement: [Etcheverry, hasVariant, Echeverri]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Echeverri Context triple: [Etcheverry, hasVariant, Echeverri]
-
A.
Lleras
Lleras is a Spanish-language surname notably associated with prominent Colombian political figures such as former president Alberto Lleras Camargo.
-
B.
Mosquera
Mosquera is a municipality in the department of Cundinamarca, Colombia, located near Bogotá and known for its growing industrial and residential development.
-
C.
Mejía
Mejía is a Spanish-language surname of Hispanic origin borne by various notable figures across Latin American history and culture.
-
D.
Echeverría
chosen
Echeverría is a Spanish-language surname borne by various notable figures in politics, literature, and the arts across the Spanish-speaking world.
-
E.
Flórez
Flórez is a Spanish-language surname most prominently associated with Peruvian operatic tenor Juan Diego Flórez.
- 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_69d6ab2eaeb881909f7914758f859413 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d90393cfb08190b5b45d3e5e32fad3 |
completed | April 10, 2026, 2:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f47209bd088190bf4c7687c0a5eed6 |
completed | May 1, 2026, 9:27 a.m. |
Created at: April 8, 2026, 9:46 p.m.