Triple

T15038922
Position Surface form Disambiguated ID Type / Status
Subject Blue’s Clues E378549 entity
Predicate creator P184 FINISHED
Object Todd Kessler E1097472 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: Todd Kessler | Statement: [Blue’s Clues, creator, Todd Kessler]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Todd Kessler
Context triple: [Blue’s Clues, creator, Todd Kessler]
  • A. Ken Kessler
    Ken Kessler is a meek, put-upon businessman who becomes entangled in a chaotic kidnapping scheme in the dark comedy film "Ruthless People."
  • B. Michael Kessler
    Michael Kessler is a German actor and comedian known for his work in film, television, and sketch comedy.
  • C. Mitch Kessler
    Mitch Kessler is a central fictional news anchor in the drama series "The Morning Show," whose fall from grace amid a sexual misconduct scandal drives much of the show's exploration of power and accountability in media.
  • D. Todd A. Kessler chosen
    Todd A. Kessler is an American television writer and producer best known for co-creating the critically acclaimed legal thriller series "Damages."
  • E. Brian Kessler
    Brian Kessler is a young writer and true-crime enthusiast who embarks on a cross-country road trip to research serial killers in the film "Kalifornia."
  • 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_69d85cd46b2c819090d054c27787f677 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded82cf3848190b0b2b6c9e65bc70b completed April 15, 2026, 12:13 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0035428e608190b8bb41dabda044d1 completed May 10, 2026, 7:35 a.m.
Created at: April 10, 2026, 2:59 a.m.