Triple
T20845273
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Esther Fernández |
E513206
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Esther Fernández |
—
|
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: Esther Fernández | Statement: [Esther Fernández, name, Esther Fernández]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Esther Fernández Context triple: [Esther Fernández, name, Esther Fernández]
-
A.
Esther Fernández
chosen
Esther Fernández was a prominent Mexican film actress known for her work during the Golden Age of Mexican cinema.
-
B.
Esther García
Esther García is a prominent Spanish film producer best known for her long-standing collaboration with director Pedro Almodóvar on acclaimed films such as "Volver."
-
C.
Esther Acebo
Esther Acebo is a Spanish actress and television presenter best known internationally for her role as Mónica Gaztambide (Stockholm) in the hit series "Money Heist."
-
D.
Esther González
Esther González is a Spanish professional footballer and prolific striker known for her clinical finishing at both club level and with the Spain women’s national team.
-
E.
Margarita Sanz
Margarita Sanz is a Mexican actress known for her acclaimed performances in film, television, and theater, often portraying complex, emotionally rich characters.
- 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_69e0b4f4898081908209e58edb8f9c45 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c34ec254819082610264c7af20c8 |
completed | April 21, 2026, 12:22 a.m. |
Created at: April 16, 2026, 12:43 p.m.