Triple

T4625976
Position Surface form Disambiguated ID Type / Status
Subject Donnie Brasco E101097 entity
Predicate editedBy P1954 FINISHED
Object Christopher Tellefsen E248156 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 Tellefsen | Statement: [Donnie Brasco, editedBy, Christopher Tellefsen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Christopher Tellefsen
Context triple: [Donnie Brasco, editedBy, Christopher Tellefsen]
  • A. Christopher Tellefsen chosen
    Christopher Tellefsen is an American film editor known for his work on acclaimed movies such as "A Quiet Place," "Moneyball," and "Capote."
  • B. Kevin Nolting
    Kevin Nolting is an American film editor best known for his work on Pixar animated features, including the Academy Award-winning film "Up."
  • C. Michael Larsen
    Michael Larsen is the person credited with coining the now-popular term “Painted Ladies” to describe the colorfully restored Victorian and Edwardian houses of San Francisco.
  • D. Greg Eklund
    Greg Eklund is an American drummer best known for his work with the alternative rock band Everclear.
  • E. Kevin Hageman
    Kevin Hageman is an American screenwriter and producer known for his work on animated and family films and television series, including contributions to The Lego Movie franchise.
  • 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_69bd43d0497c8190ac23c65c5804846a completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd5a0a7b588190bc6552ee5babb198 completed March 20, 2026, 2:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf7fc5ae2c819085a18354feaa6150 completed March 22, 2026, 5:36 a.m.
Created at: March 20, 2026, 1:13 p.m.