Triple

T9923038
Position Surface form Disambiguated ID Type / Status
Subject Cheek to Cheek E187847 entity
Predicate performedBy P1363 FINISHED
Object Tony Bennett E60140 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: Tony Bennett | Statement: [Cheek to Cheek, performedBy, Tony Bennett]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tony Bennett
Context triple: [Cheek to Cheek, performedBy, Tony Bennett]
  • A. Tony Bennett chosen
    Tony Bennett was an American traditional pop and jazz singer renowned for his smooth vocal style and timeless standards like "I Left My Heart in San Francisco."
  • B. Mel Tormé
    Mel Tormé was an American jazz singer, composer, and actor, celebrated for his smooth vocal style and known as "The Velvet Fog."
  • C. Frank Sinatra
    Frank Sinatra was an iconic American singer and actor renowned for his smooth baritone voice, classic pop and jazz recordings, and influential film roles.
  • D. Johnny Hartman
    Johnny Hartman was an American jazz baritone vocalist renowned for his rich, velvety tone and acclaimed collaborations with artists such as John Coltrane.
  • E. Sinatra
    Sinatra is a lightweight Ruby web application framework known for its simple, DSL-based approach to building web services and APIs.
  • 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_69ca82b22a688190b52c75bd48429c10 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cdb59733188190900426e4e29ae5e3 completed April 2, 2026, 12:17 a.m.
NED1 Entity disambiguation (via context triple) batch_69d228c23a2c81908fa2cb3a4f90d198 completed April 5, 2026, 9:17 a.m.
Created at: March 30, 2026, 8:42 p.m.