Triple

T13840801
Position Surface form Disambiguated ID Type / Status
Subject Lido de Paris E332649 entity
Predicate foundedBy P104 FINISHED
Object Joseph Clerico E65642 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: Joseph Clerico | Statement: [Lido de Paris, foundedBy, Joseph Clerico]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Joseph Clerico
Context triple: [Lido de Paris, foundedBy, Joseph Clerico]
  • A. Joseph Clerico chosen
    Joseph Clerico was a French impresario best known for co-founding and developing the famed Lido de Paris cabaret into one of Paris’s most iconic nightlife institutions.
  • B. Robert Cohl
    Robert Cohl is the son of Canadian film producer and Toronto International Film Festival co-founder Dusty Cohl.
  • C. Mark Leno
    Mark Leno is an American Democratic politician and former California state legislator known for representing San Francisco in both the State Assembly and State Senate.
  • D. Paul Marino
    Paul Marino is an individual notable enough to be recognized as a significant bearer of the surname Marino, though specific widely known public details about him are not clearly established.
  • E. Jeremy Sisto
    Jeremy Sisto is an American actor known for his roles in film and television, including prominent parts in projects like the teen comedy "Clueless" and the crime drama series "Law & Order."
  • 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_69d81c5ae7c88190b0dd41bdafeb5999 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de02ae5e4c8190ad85ad2968bc71b2 completed April 14, 2026, 9:02 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7b8f630d081909439e1cdc5d60430 completed May 3, 2026, 9:07 p.m.
Created at: April 9, 2026, 10:13 p.m.