Triple

T13026371
Position Surface form Disambiguated ID Type / Status
Subject George Nader E326317 entity
Predicate partner P1136 FINISHED
Object Mark Miller E1028484 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: Mark Miller | Statement: [George Nader, partner, Mark Miller]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mark Miller
Context triple: [George Nader, partner, Mark Miller]
  • A. Mark Miller chosen
    Mark Miller is an American actor and screenwriter known for his work in film and television from the mid-20th century onward.
  • B. Nick Miller
    Nick Miller is a gruff yet endearing, underachieving bartender and aspiring writer who serves as one of the central roommates and comedic leads in the sitcom "New Girl."
  • C. Ken Miller
    Ken Miller is a laid-back, sarcastic high school student and one of the core "freak" friends in the cult TV series *Freaks and Geeks*.
  • D. Andrew Miller
    Andrew Miller is an American former Major League Baseball relief pitcher known for his dominant left-handed pitching and key postseason performances for multiple teams, including the Cleveland Indians and New York Yankees.
  • E. Neil Miller
    Neil Miller is a skeptical psychiatrist and the stepfather figure who provides comic tension and emotional contrast in the Christmas film "The Santa Clause."
  • 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_69d8076cc45c81908123123f43e69266 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d97efc07488190a15f3e41ea2db45c completed April 10, 2026, 10:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69f716b885708190b6c38c481fa9ca21 completed May 3, 2026, 9:34 a.m.
Created at: April 9, 2026, 8:53 p.m.