Triple

T8428124
Position Surface form Disambiguated ID Type / Status
Subject Laundry Service E199052 entity
Predicate producer P490 FINISHED
Object Lester Mendez E742627 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: Lester Mendez | Statement: [Laundry Service, producer, Lester Mendez]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lester Mendez
Context triple: [Laundry Service, producer, Lester Mendez]
  • A. Lester Méndez chosen
    Lester Méndez is a Grammy-winning Cuban-American record producer and songwriter known for his influential work in Latin pop and collaborations with major artists like Shakira.
  • B. Avelino Arredondo
    Avelino Arredondo is a short story by Jorge Luis Borges that fictionalizes the life of the historical Uruguayan assassin who killed President Juan Idiarte Borda.
  • C. George A. Mendoza
    George A. Mendoza is a film producer best known for his work on Disney’s animated feature "The Lion King 1½."
  • D. Frank J. Urioste
    Frank J. Urioste is an American film editor known for his work on major action and genre films, including blockbusters like Die Hard, RoboCop, and Total Recall.
  • E. Moises Arias
    Moises Arias is an American actor best known for his roles in "Hannah Montana," "The Kings of Summer," and various independent films.
  • 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_69ca8313c99081909a5c6d83b91de5b3 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbd124378c819086ea2fa6ecbfffe1 completed March 31, 2026, 1:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69cea82225148190b9dd190655114c8a completed April 2, 2026, 5:32 p.m.
Created at: March 30, 2026, 6:07 p.m.