Triple

T20898388
Position Surface form Disambiguated ID Type / Status
Subject Bloodline E514603 entity
Predicate creator P184 FINISHED
Object Todd A. Kessler 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: Todd A. Kessler | Statement: [Bloodline, creator, Todd A. Kessler]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Todd A. Kessler
Context triple: [Bloodline, creator, Todd A. Kessler]
  • A. 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."
  • B. Stephen Kessler
    Stephen Kessler is an American film and television director best known for his work on comedies, including the National Lampoon’s Vegas Vacation.
  • C. Kevin A. Ross
    Kevin A. Ross is an American television personality and former judge best known for presiding over the syndicated courtroom show "America’s Court with Judge Ross."
  • D. Michael J. Pierson
    Michael J. Pierson is an individual notable enough to be specifically cited as a namesake or distinguished bearer of the surname Pierson.
  • E. Stephen M. Kellen
    Stephen M. Kellen was a prominent financier and philanthropist known for his leadership at Arnhold and S. Bleichroeder and his significant support of cultural and educational institutions.
  • 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_69e0b4f7ebe48190952a85547a0f31a1 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6e8f826788190b11008cc94b2a4e4 completed April 21, 2026, 3:03 a.m.
Created at: April 16, 2026, 12:47 p.m.