Triple
T10586178
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miguel A. Núñez Jr. |
E249860
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Núñez |
E712509
|
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: Núñez | Statement: [Miguel A. Núñez Jr., familyName, Núñez]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Núñez Context triple: [Miguel A. Núñez Jr., familyName, Núñez]
-
A.
Núñez
chosen
Núñez is a residential neighborhood in northern Buenos Aires, Argentina, best known in sports for hosting River Plate’s Monumental stadium.
-
B.
Núñez Vela
Núñez Vela is a Spanish surname most notably associated with Blasco Núñez Vela, the first viceroy of Peru in the 16th century.
-
C.
Quiñonez
Quiñonez is the surname of actor Tony Revolori, known for his role in "The Grand Budapest Hotel."
-
D.
Montero Ríos
Montero Ríos is the surname of Eugenio Montero Ríos, a prominent Spanish jurist and politician who served as Prime Minister of Spain in the early 20th century.
-
E.
Néstor
Néstor is a masculine given name of Spanish origin, commonly used in Spanish-speaking countries.
- 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_69d381c9d3d48190a29ee491e1696a0e |
completed | April 6, 2026, 9:50 a.m. |
| NER | Named-entity recognition | batch_69d527698be48190a2f5e573d7cc0661 |
completed | April 7, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d94b8b1b708190865e428128f98720 |
completed | April 10, 2026, 7:12 p.m. |
Created at: April 6, 2026, 12:39 p.m.