Triple
T13838946
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vázquez |
E332600
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Vazquez |
E332600
|
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: Vazquez | Statement: [Vázquez, hasVariant, Vazquez]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vazquez Context triple: [Vázquez, hasVariant, Vazquez]
-
A.
Vázquez
chosen
Vázquez is a Spanish-language surname commonly found in Spain and Latin America, borne by various notable figures in entertainment, sports, and public life.
-
B.
Balderas
Balderas is a major Mexico City Metro station known for its central location and high passenger traffic.
-
C.
Garza
Garza is a Spanish-language surname of Basque origin that is common in Mexico and among people of Hispanic heritage.
-
D.
Quiñonez
Quiñonez is the surname of actor Tony Revolori, known for his role in "The Grand Budapest Hotel."
-
E.
Vásquez
Vásquez is a Spanish-language surname common in Latin America and Spain, borne by numerous notable figures in sports, politics, and the arts.
- 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_69d81c5ae7c88190b0dd41bdafeb5999 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de02ac6b7c81908d44632d6d628339 |
completed | April 14, 2026, 9:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7c7062f548190a6a8d06ef2eefc9f |
completed | May 3, 2026, 10:07 p.m. |
Created at: April 9, 2026, 10:13 p.m.