Triple
T15410645
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bernardo Márquez García |
E368580
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Bernardo Márquez García |
—
|
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: Bernardo Márquez García | Statement: [Bernardo Márquez García, name, Bernardo Márquez García]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bernardo Márquez García Context triple: [Bernardo Márquez García, name, Bernardo Márquez García]
-
A.
Bernardo Márquez García
chosen
Bernardo Márquez García is a Puerto Rican politician who serves as the mayor of the municipality of Toa Baja.
-
B.
Bernardo Flores Sisk
Bernardo Flores Sisk was a U.S. Congressman from California who played a key role in Central Valley water projects and for whom the San Luis Dam was named.
-
C.
Antonio Medrano
Antonio Medrano was an architect known for his work on the Royal Palace of Portici in Italy.
-
D.
Miguel Ordóñez
Miguel Ordóñez is an illustrator known for his playful, minimalist artwork in children’s books and other visual storytelling projects.
-
E.
Fernando Argüelles
Fernando Argüelles is a cinematographer known for his work on films such as "Doppelganger."
- 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_69d85a16c68c819099c1b547fbc87b32 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03ea600b48190a3dbca1a68a2a1cd |
completed | April 16, 2026, 1:43 a.m. |
Created at: April 10, 2026, 3:20 a.m.