Triple

T10211397
Position Surface form Disambiguated ID Type / Status
Subject The Racketeer E242335 entity
Predicate hasCharacter P2308 FINISHED
Object Malcolm Bannister E850159 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: Malcolm Bannister | Statement: [The Racketeer, hasCharacter, Malcolm Bannister]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Malcolm Bannister
Context triple: [The Racketeer, hasCharacter, Malcolm Bannister]
  • A. Malcolm Bannister chosen
    Malcolm Bannister is a former attorney turned imprisoned whistleblower who becomes the clever, morally ambiguous protagonist of John Grisham’s legal thriller "The Racketeer."
  • B. Malcolm Blight
    Malcolm Blight is a legendary Australian rules footballer and coach, renowned for his brilliant playing career and innovative coaching in the VFL/AFL.
  • C. Malcolm Weir
    Malcolm Weir is a British scientist and biotechnology entrepreneur best known as the founder of the drug discovery company Heptares Therapeutics.
  • D. Malcolm Dixon
    Malcolm Dixon was a British actor and dwarf performer best known for his roles in fantasy and science-fiction films of the late 20th century.
  • E. Malcolm Delaney
    Malcolm Delaney is an American professional basketball player known for his standout college career at Virginia Tech and his subsequent play in the NBA and top European leagues.
  • 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.