Triple

T10211161
Position Surface form Disambiguated ID Type / Status
Subject The Broker E242329 entity
Predicate hasCharacter P2308 FINISHED
Object Joel Backman E850144 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: Joel Backman | Statement: [The Broker, hasCharacter, Joel Backman]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Joel Backman
Context triple: [The Broker, hasCharacter, Joel Backman]
  • A. Joel Backman chosen
    Joel Backman is a powerful and controversial Washington lawyer and lobbyist who becomes the hunted protagonist in John Grisham’s legal thriller "The Broker."
  • B. Jeff Malmberg
    Jeff Malmberg is an American documentary filmmaker best known for directing the acclaimed film "Marwencol," which inspired the narrative of "Welcome to Marwen."
  • C. Greg Eklund
    Greg Eklund is an American drummer best known for his work with the alternative rock band Everclear.
  • D. Jon Ekstrand
    Jon Ekstrand is a Swedish film composer and sound designer known for his atmospheric scores for documentaries and feature films, including collaborations with director Daniel Espinosa.
  • E. Daniel Nannskog
    Daniel Nannskog is a retired Swedish striker best known for his prolific goal-scoring spell at Norwegian club Stabæk Fotball and later work as a football pundit.
  • 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_69d381ae26c48190985abd0e25ee5d04 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d3aa22071c819095febd18dd607978 completed April 6, 2026, 12:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69d6a7f6730081908b941eaeb6c00993 completed April 8, 2026, 7:09 p.m.
Created at: April 6, 2026, 11:01 a.m.