Triple
T15613379
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rafael Pérez |
E375353
|
entity |
| Predicate | fullName |
P16
|
FINISHED |
| Object | Rafael Antonio Pérez |
—
|
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: Rafael Antonio Pérez | Statement: [Rafael Pérez, fullName, Rafael Antonio Pérez]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rafael Antonio Pérez Context triple: [Rafael Pérez, fullName, Rafael Antonio Pérez]
-
A.
Rafael Pérez
chosen
Rafael Pérez is a former Los Angeles Police Department officer whose central role in exposing widespread corruption and misconduct made him a key figure in the Rampart scandal.
-
B.
Rafael Martinez
Rafael Martinez is an actor known for his role in the inspirational sports drama film "McFarland, USA."
-
C.
Rafael Acosta
Rafael Acosta is a central figure in Luis Buñuel’s surrealist satire "The Discreet Charm of the Bourgeoisie," embodying the absurdities and hypocrisies of upper-middle-class society.
-
D.
Roberto Morales
Roberto Morales is a film and television producer known for his work on the project "Vivir."
-
E.
Rafael Pineda
Rafael Pineda is a songwriter best known for co-writing Beyoncé’s hit track “Cuff It.”
- 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_69d85ccf2794819096cda4cbcb02d478 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e83407c8190abbcd4b7fab0ff85 |
completed | April 16, 2026, 2:50 a.m. |
Created at: April 10, 2026, 4:13 a.m.