Triple
T14655516
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bad Education |
E344097
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Fele Martínez |
E1173193
|
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: Fele Martínez | Statement: [Bad Education, starring, Fele Martínez]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fele Martínez Context triple: [Bad Education, starring, Fele Martínez]
-
A.
Fele Martínez
chosen
Fele Martínez is a Spanish actor known for his roles in acclaimed films of the 1990s and 2000s, particularly in psychological dramas and thrillers.
-
B.
Mercédès Herrera
Mercédès Herrera is a fictional character from Alexandre Dumas' novel "The Count of Monte Cristo," known as Edmond Dantès' former fiancée who later marries Fernand Mondego.
-
C.
Mariola Fuentes
Mariola Fuentes is a Spanish actress known for her character roles in films and television, particularly in the works of director Pedro Almodóvar.
-
D.
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.
-
E.
Yolanda Ramos
Yolanda Ramos is a Spanish actress and comedian known for her work in television, film, and theater, particularly in Spanish comedy shows and movies.
- 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_69d822e1a2cc81908e5bb93cf61ce3cc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb51a562c819098971447db4b29f7 |
completed | April 14, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a001f79c6ac81909935dace3dcc8bea |
completed | May 10, 2026, 6:02 a.m. |
Created at: April 10, 2026, 1:27 a.m.