Triple

T3086841
Position Surface form Disambiguated ID Type / Status
Subject For Once in My Life E64391 entity
Predicate notableRecordingBy P1152 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: [For Once in My Life, notableRecordingBy, Tony Bennett]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tony Bennett
Context triple: [For Once in My Life, notableRecordingBy, 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. Sinatra
    Sinatra is a lightweight Ruby web application framework known for its simple, DSL-based approach to building web services and APIs.
  • E. Nat King Cole
    Nat King Cole was an American jazz pianist and velvety-voiced pop singer who became one of the most influential and popular entertainers of the mid-20th century.
  • 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_69ad857c97d88190b26f9b1c90839c77 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada1ec187c819084565514ebb99cdb completed March 8, 2026, 4:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69b1f89e6f5c8190993794e2c9977ee6 completed March 11, 2026, 11:19 p.m.
Created at: March 8, 2026, 3:03 p.m.