Triple
T22217767
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Javier Cámara |
E549121
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Javier Cámara |
—
|
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: Javier Cámara | Statement: [Javier Cámara, name, Javier Cámara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Javier Cámara Context triple: [Javier Cámara, name, Javier Cámara]
-
A.
Javier Cámara
chosen
Javier Cámara is a Spanish actor known for his acclaimed performances in both film and television, including prominent roles in Pedro Almodóvar’s movies.
-
B.
Miguel Ángel Ramírez
Miguel Ángel Ramírez is a Spanish football manager known for his tactical work in South American and Major League Soccer clubs.
-
C.
Raul Daza
Raul Daza is a Filipino lawyer and veteran politician known for his long-standing service as a congressman and leader within the Liberal Party of the Philippines.
-
D.
Greg Garcia
Greg Garcia is an American television writer and producer best known for creating the sitcom "My Name Is Earl."
-
E.
Daniel Padilla
Daniel Padilla is a popular Filipino actor and recording artist known for his leading roles in television dramas and films, as well as his successful music career.
- 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_69e11e403d6481909a94d0aaf157f6ef |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12b8ddb448190a45f0418d813afd0 |
completed | April 28, 2026, 9:50 p.m. |
Created at: April 16, 2026, 8:37 p.m.