Triple

T6239444
Position Surface form Disambiguated ID Type / Status
Subject Kristin (TV series) E139561 entity
Predicate executiveProducer P7225 FINISHED
Object John Markus E582349 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: John Markus | Statement: [Kristin (TV series), executiveProducer, John Markus]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John Markus
Context triple: [Kristin (TV series), executiveProducer, John Markus]
  • A. John Markus chosen
    John Markus is an American television writer and producer best known for his work on sitcoms such as The Cosby Show and for creating the series Kristin.
  • B. Markus Morgenstern
    Markus Morgenstern is a mathematician known for his contributions to combinatorics and graph theory.
  • C. Markus
    Markus is the given first name of the renowned abstract expressionist painter Mark Rothko.
  • D. Markus Wolf
    Markus Wolf was a prominent East German spymaster who led the foreign intelligence service of the Stasi and became one of the Cold War’s most influential intelligence chiefs.
  • E. Jack Tornek
    Jack Tornek was a character actor known for small roles in early 20th-century American films, including the 1950s horror movie "Hellgate."
  • 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_69c008b0e7ac8190808a59573ee646f3 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c063048df081909a13d16b6f6bf65d completed March 22, 2026, 9:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69c5e3e5bd988190ad9b0af668f5b05c completed March 27, 2026, 1:56 a.m.
Created at: March 22, 2026, 4:23 p.m.