Triple
T13710129
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ana Navarro |
E328748
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Ana Navarro |
E328748
|
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: Ana Navarro | Statement: [Ana Navarro, name, Ana Navarro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ana Navarro Context triple: [Ana Navarro, name, Ana Navarro]
-
A.
Ana Navarro
chosen
Ana Navarro is a Nicaraguan-American Republican strategist, political commentator, and television personality known for her outspoken views on U.S. politics.
-
B.
Silvia Navarro
Silvia Navarro is a Mexican actress best known for her leading roles in popular telenovelas and television dramas.
-
C.
Ana Valenzuela
Ana Valenzuela is a notable individual distinguished enough within her field or public life to be recognized as a prominent bearer of the Valenzuela surname.
-
D.
Elvira Ramírez
Elvira Ramírez was a 10th-century Leonese infanta and powerful regent of the Kingdom of León, known for governing on behalf of her young nephew Ramiro III.
-
E.
Anna Morales
Anna Morales is a tough, pragmatic businesswoman and the morally ambiguous wife of an ambitious heating-oil entrepreneur in the crime drama film "A Most Violent Year."
- 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_69d80770b9bc81909f70c8c317d53cff |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dd43949e6c8190ae5e4fa119cde33a |
completed | April 13, 2026, 7:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdd5b543308190a86e715106641484 |
completed | May 8, 2026, 12:23 p.m. |
Created at: April 9, 2026, 9:54 p.m.