Triple

T13006144
Position Surface form Disambiguated ID Type / Status
Subject John Magaro E322291 entity
Predicate notableWork P4 FINISHED
Object Lansky E22593 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: Lansky | Statement: [John Magaro, notableWork, Lansky]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lansky
Context triple: [John Magaro, notableWork, Lansky]
  • A. Meyer Lansky chosen
    Meyer Lansky was a major American organized crime figure and financial mastermind who helped build the National Crime Syndicate and modernize the mob’s gambling operations.
  • B. Lepke
    Lepke was the underworld nickname of Louis Buchalter, a notorious American mobster and leader of the contract killing organization Murder, Inc.
  • C. Mickey Cohen
    Mickey Cohen was a notorious mid-20th-century Los Angeles mobster and former associate of Bugsy Siegel who became a prominent figure in organized crime on the West Coast.
  • D. Dutch Schultz
    Dutch Schultz was a notorious Prohibition-era American mobster and bootlegger who became a prominent figure in organized crime before his murder in 1935.
  • E. Ronnie Rothstein
    Ronnie Rothstein is a bridal industry expert and co-owner of New York’s Kleinfeld Bridal, known for appearing as a consultant on the reality TV show "Say Yes to the Dress."
  • 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_69d807657e8c8190bd9435ee2f823845 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d97e9b27ec8190815c40a05b9ba7d0 completed April 10, 2026, 10:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6c109e59481909fc46b152034c6a9 completed May 3, 2026, 3:29 a.m.
Created at: April 9, 2026, 8:48 p.m.