Triple
T19982376
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Babel (2006 film) |
E493847
|
entity |
| Predicate | cinematographer |
P1953
|
FINISHED |
| Object | Rodrigo Prieto |
—
|
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: Rodrigo Prieto | Statement: [Babel (2006 film), cinematographer, Rodrigo Prieto]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rodrigo Prieto Context triple: [Babel (2006 film), cinematographer, Rodrigo Prieto]
-
A.
Rodrigo Prieto
chosen
Rodrigo Prieto is a renowned Mexican cinematographer known for his visually distinctive work on major films by directors such as Martin Scorsese and Alejandro G. Iñárritu.
-
B.
Miguel Ordóñez
Miguel Ordóñez is an illustrator known for his playful, minimalist artwork in children’s books and other visual storytelling projects.
-
C.
Pablo Galindo
Pablo Galindo is a Python core developer and software engineer known for his work on the language’s internals, including co-authoring structural pattern matching (PEP 634) and contributing extensively to CPython.
-
D.
Guillermo Pulido
Guillermo Pulido is a notable individual whose surname, Pulido, is recognized as being borne by him.
-
E.
Roberto Álamo
Roberto Álamo is a Spanish actor known for his work in film, television, and theater, including prominent roles in acclaimed Spanish cinema.
- 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_69da626a67648190af9653832a3aeced |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e65d13a8a88190bf5f4f697793f4c9 |
completed | April 20, 2026, 5:06 p.m. |
Created at: April 11, 2026, 3:28 p.m.