Triple

T12183163
Position Surface form Disambiguated ID Type / Status
Subject Children's Television Workshop E290268 entity
Predicate employed P7 FINISHED
Object Christopher Cerf E250875 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: Christopher Cerf | Statement: [Children's Television Workshop, employed, Christopher Cerf]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Christopher Cerf
Context triple: [Children's Television Workshop, employed, Christopher Cerf]
  • A. Christopher Cerf chosen
    Christopher Cerf is an American writer, composer, and producer best known for his longtime creative work on Sesame Street and other educational media.
  • B. Richard Lederer
    Richard Lederer is a film producer best known for his work on major studio projects, including the horror sequel "Exorcist II: The Heretic."
  • C. Michael Tolkin
    Michael Tolkin is an American screenwriter, director, and novelist best known for works such as "The Player" and his contributions to critically acclaimed film and television projects.
  • D. Peter Ochs
    Peter Ochs was an 18th-century Swiss politician and reformer best known for helping to establish the Helvetic Republic.
  • E. M.G. Siegler
    M.G. Siegler is a technology writer and venture capitalist known for his work at TechCrunch and his investing role at Google Ventures (GV).
  • 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_69d6ab64de5881908d56eb7a75c6cc69 completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d915fd8dac8190928059ad2b6bbbf3 completed April 10, 2026, 3:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69f5f6acee6481908ee4129956c98c52 completed May 2, 2026, 1:05 p.m.
Created at: April 8, 2026, 9:50 p.m.