Triple
T19455879
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Javier Peña |
E486731
|
entity |
| Predicate | basedOn |
P98
|
FINISHED |
| Object | Javier F. Peña |
—
|
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 F. Peña | Statement: [Javier Peña, basedOn, Javier F. Peña]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Javier F. Peña Context triple: [Javier Peña, basedOn, Javier F. Peña]
-
A.
Javier F. Peña
chosen
Javier F. Peña is a retired American DEA agent best known for his role in the pursuit of Colombian drug lord Pablo Escobar and for being portrayed in the Netflix series "Narcos."
-
B.
Santiago E. Campos
Santiago E. Campos was a United States federal judge whose judicial service and legacy led to a federal courthouse being named in his honor.
-
C.
Pablo A. Gallina
Pablo A. Gallina is an Argentine paleontologist known for his research on South American dinosaurs and collaboration with other leading researchers in the field.
-
D.
J. David López-Salido
J. David López-Salido is an economist known for his coauthored research in macroeconomics and monetary policy, including work with Jordi Galí.
-
E.
Jorge A. Jimenez
Jorge A. Jimenez is an actor known for his role in the action drama film "Mercury Plains."
- 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_69d8e8d86d608190bd199a98d0297f27 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e633c2b1108190b492ca23487b91f8 |
completed | April 20, 2026, 2:10 p.m. |
Created at: April 10, 2026, 1:38 p.m.