Triple
T21386260
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cristo Fernández |
E527506
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Cristo 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: Cristo Fernández | Statement: [Cristo Fernández, name, Cristo Fernández]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cristo Fernández Context triple: [Cristo Fernández, name, Cristo Fernández]
-
A.
Cristo Fernández
chosen
Cristo Fernández is a Mexican actor and former professional footballer best known for playing the exuberant footballer Dani Rojas on the television series "Ted Lasso."
-
B.
Sergio Rouco
Sergio Rouco is an American college basketball coach best known for leading the Florida International University (FIU) men's basketball program in the mid-2000s.
-
C.
José Gómez
José Gómez was a figure significant enough in Chilean or maritime history that the remote Pacific island Salas y Gómez was named in his honor.
-
D.
Luis Carballar
Luis Carballar is a film editor best known for his work on the acclaimed Mexican drama "Amores perros."
-
E.
Andrés Rojo
Andrés Rojo is an individual notable enough to be recognized as a prominent bearer of the surname Rojo.
- 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_69e0b51f363c8190944000ab5523b02b |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e8b0f3d37c8190b43ec77cdb1904c8 |
completed | April 22, 2026, 11:28 a.m. |
Created at: April 16, 2026, 5:12 p.m.