Triple
T19802504
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Valle de los Caídos |
E475718
|
entity |
| Predicate | architect |
P184
|
FINISHED |
| Object | Diego Méndez |
—
|
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: Diego Méndez | Statement: [Valle de los Caídos, architect, Diego Méndez]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Diego Méndez Context triple: [Valle de los Caídos, architect, Diego Méndez]
-
A.
Diego Méndez
chosen
Diego Méndez was a Spanish architect best known for co-designing the monumental Franco-era basilica and memorial complex at the Valle de los Caídos near Madrid.
-
B.
Diego Fagúndez
Diego Fagúndez is a Uruguayan-American professional soccer midfielder known for his long tenure in Major League Soccer, particularly with the New England Revolution and later Austin FC.
-
C.
Diego Vasquez
Diego Vasquez is a fictional character portrayed by Brazilian actor Bruno Campos, best known from his work in American television and film.
-
D.
Diego Soto
Diego Soto is a fictional character portrayed by actor Jorge Garcia, best known as a comic-book author and Alcatraz expert on the TV series "Alcatraz."
-
E.
Diego Navarro
Diego Navarro is a Spanish film composer known for his orchestral scores for movies and television.
- 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_69d8e51bc4208190a1c57d8c5d1b15e4 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e654257cb4819096fb2aa5d1f7fbb0 |
completed | April 20, 2026, 4:28 p.m. |
Created at: April 10, 2026, 1:49 p.m.