Triple
T5218189
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Silvia Navarro |
E117804
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Silvia Navarro |
E117804
|
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: Silvia Navarro | Statement: [Silvia Navarro, name, Silvia Navarro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Silvia Navarro Context triple: [Silvia Navarro, name, Silvia Navarro]
-
A.
Silvia Navarro
chosen
Silvia Navarro is a Mexican actress best known for her leading roles in popular telenovelas and television dramas.
-
B.
Ana Navarro
Ana Navarro is a Nicaraguan-American Republican strategist, political commentator, and television personality known for her outspoken views on U.S. politics.
-
C.
Michelle Navarro
Michelle Navarro is an individual notable enough to be recognized as a prominent bearer of the Navarro surname.
-
D.
Marta Navarro
Marta Navarro is a personal name that may refer to multiple individuals across different fields, such as sports, arts, or public life, rather than a single widely recognized figure.
-
E.
Paola Núñez
Paola Núñez is a Mexican actress and producer known for her work in telenovelas and English-language television and film, including prominent roles in series like The Purge and the film Bad Boys for Life.
- 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_69bd4465e03081909bfcfd7113062590 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7a96d49c8190a58726a57edebdcc |
completed | March 20, 2026, 4:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf06b04cd881909e31b4e533dc4ae8 |
completed | March 21, 2026, 8:59 p.m. |
Created at: March 20, 2026, 1:48 p.m.