Triple

T2830466
Position Surface form Disambiguated ID Type / Status
Subject Rio E62222 entity
Predicate voiceCastMember P9616 FINISHED
Object George Lopez E49202 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: George Lopez | Statement: [Rio, voiceCastMember, George Lopez]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: George Lopez
Context triple: [Rio, voiceCastMember, George Lopez]
  • A. George Lopez chosen
    George Lopez is an American comedian and actor known for his stand-up focusing on Mexican-American culture and for creating and starring in the sitcom "George Lopez."
  • B. John Leguizamo
    John Leguizamo is a Colombian-American actor, comedian, and producer known for his energetic character roles in film, television, and theater, as well as his acclaimed one-man shows.
  • C. Larry Franco
    Larry Franco is an American film producer known for his work on major Hollywood movies, including action, science fiction, and comic book adaptations.
  • D. Wilmer Valderrama
    Wilmer Valderrama is an American actor, producer, and television personality best known for his role as Fez on "That '70s Show" and for his extensive work in film and voice acting.
  • E. Michael Peña
    Michael Peña is an American actor known for his versatile supporting roles in films such as "Crash," "Ant-Man," and "End of Watch."
  • 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_69ab4c3c39188190955b9c49d98463d8 completed March 6, 2026, 9:50 p.m.
NER Named-entity recognition batch_69abdebd5a2c81908f0e30a0ae0eb8df completed March 7, 2026, 8:15 a.m.
NED1 Entity disambiguation (via context triple) batch_69afceb771d48190a1467a6e58f756ad completed March 10, 2026, 7:56 a.m.
Created at: March 6, 2026, 10:01 p.m.