Triple

T19456209
Position Surface form Disambiguated ID Type / Status
Subject RoboCop (2014 film) E486738 entity
Predicate editor P1954 FINISHED
Object Daniel Rezende 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: Daniel Rezende | Statement: [RoboCop (2014 film), editor, Daniel Rezende]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Daniel Rezende
Context triple: [RoboCop (2014 film), editor, Daniel Rezende]
  • A. Daniel Rezende chosen
    Daniel Rezende is a Brazilian film editor best known for his acclaimed work on internationally recognized films such as "City of God" and other major cinematic projects.
  • B. Miguel Fisac
    Miguel Fisac was a prominent 20th-century Spanish architect known for his innovative use of concrete and expressive structural forms in religious and public buildings.
  • C. Erick M. Carreira
    Erick M. Carreira is a prominent organic chemist known for his contributions to synthetic methodology and natural product synthesis, and for serving as editor-in-chief of the journal Angewandte Chemie.
  • D. Erico Menczer
    Erico Menczer was an Italian cinematographer known for his work on European films in the 1960s and 1970s.
  • E. Marcos A. Ferraez
    Marcos A. Ferraez is an American actor and writer known for roles in television series such as "Pacific Blue" and various guest appearances on popular TV shows.
  • 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_69e633c4088881908f23f25a82a513f6 completed April 20, 2026, 2:10 p.m.
Created at: April 10, 2026, 1:38 p.m.