Triple

T11996347
Position Surface form Disambiguated ID Type / Status
Subject Amores perros E285539 entity
Predicate cinematographyBy P1953 FINISHED
Object Rodrigo Prieto E151442 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: Rodrigo Prieto | Statement: [Amores perros, cinematographyBy, Rodrigo Prieto]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rodrigo Prieto
Context triple: [Amores perros, cinematographyBy, 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. Jose Villarrubia
    Jose Villarrubia is a Spanish-born comic book colorist and artist renowned for his atmospheric, painterly work on numerous high-profile titles for major publishers like Marvel and DC.
  • 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_69d6ab44a77c8190a652f4b27164e4ef completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d903c172788190b92042e9d10a48bf completed April 10, 2026, 2:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6e2548a848190a3a72415a5e4d0fd completed May 3, 2026, 5:51 a.m.
Created at: April 8, 2026, 9:46 p.m.