Triple
T20492991
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cuando seas mía |
E502793
|
entity |
| Predicate | helpedEstablishFameOf |
P57277
|
FINISHED |
| Object | Silvia Navarro |
—
|
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: Silvia Navarro | Statement: [Cuando seas mía, helpedEstablishFameOf, Silvia Navarro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Silvia Navarro Context triple: [Cuando seas mía, helpedEstablishFameOf, 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.
Marialy Rivas
Marialy Rivas is a Chilean film director known for her bold, socially engaged storytelling and acclaimed works such as the feature film "Young & Wild."
-
E.
Marina Castaño
Marina Castaño is a Spanish journalist and writer best known as the widow of Nobel Prize–winning author Camilo José Cela.
- 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_69e0b4b0373881909dd3e9387f82eab4 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e69cbb3bd081909351525208b41bba |
completed | April 20, 2026, 9:38 p.m. |
Created at: April 16, 2026, 11:35 a.m.