Triple
T5473391
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nicolás Lodeiro |
E122889
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Nicolás |
E77168
|
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: Nicolás | Statement: [Nicolás Lodeiro, givenName, Nicolás]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nicolás Context triple: [Nicolás Lodeiro, givenName, Nicolás]
-
A.
Nicolás
chosen
Nicolás is a masculine given name of Greek origin, commonly used in Spanish-speaking countries and derived from the name Nicholas, meaning "victory of the people."
-
B.
Felipe Fermín Paúl
Felipe Fermín Paúl was a Venezuelan statesman and independence leader who helped shape the country’s break from Spanish colonial rule in the early 19th century.
-
C.
Mariano
Mariano is a masculine given name of Spanish and Portuguese origin, commonly used in various Spanish-speaking and Latin cultures.
-
D.
Enrique
Enrique is a Spanish given name equivalent to the English name Henry.
-
E.
Juan Antonio
Juan Antonio is a charismatic Spanish painter and romantic lead in the film "Vicky Cristina Barcelona," whose passionate relationships drive much of the movie’s drama.
- 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_69bd46459ff48190823377457bcf7128 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd9230d6d88190a9ac48a4488e1754 |
completed | March 20, 2026, 6:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf48982b348190907c11424559cf76 |
completed | March 22, 2026, 1:40 a.m. |
Created at: March 20, 2026, 2:09 p.m.