Triple

T18234133
Position Surface form Disambiguated ID Type / Status
Subject Ruthless E436623 entity
Predicate screenwriter P2831 FINISHED
Object Gordon Kahn NE NERFINISHED

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: Gordon Kahn | Statement: [Ruthless, screenwriter, Gordon Kahn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gordon Kahn
Context triple: [Ruthless, screenwriter, Gordon Kahn]
  • A. Gordon Kahn chosen
    Gordon Kahn was an American screenwriter and journalist best known for his work in Hollywood during the 1930s and 1940s and for being blacklisted during the McCarthy era.
  • B. Allan Kayser
    Allan Kayser is an American actor best known for playing Bubba Higgins on the sitcom "Mama’s Family."
  • C. Hank Greenspun
    Hank Greenspun was an American newspaper publisher, political activist, and philanthropist best known as the influential owner of the Las Vegas Sun.
  • D. Leonard Bosack
    Leonard Bosack is an American computer engineer and entrepreneur best known as the co-founder of Cisco Systems, a pioneering company in computer networking and internet infrastructure.
  • E. Jack Schwartz
    Jack Schwartz was an American mathematician and computer scientist known for his contributions to programming languages, parallel computing, and the development of the SETL language.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8b9103a8081908bbb0836fef10efd completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4f4b512a88190aa493b0793ab28b3 completed April 19, 2026, 3:28 p.m.
Created at: April 10, 2026, 10:33 a.m.