Triple

T10402468
Position Surface form Disambiguated ID Type / Status
Subject Marv Albert E245180 entity
Predicate fullName P16 FINISHED
Object Marv Albert E245180 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: Marv Albert | Statement: [Marv Albert, fullName, Marv Albert]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Marv Albert
Context triple: [Marv Albert, fullName, Marv Albert]
  • A. Marv Albert chosen
    Marv Albert is a renowned American sportscaster best known as the longtime voice of NBA basketball and a prominent play-by-play announcer across multiple major sports.
  • B. Brent Musburger
    Brent Musburger is an American sportscaster best known for his long career as a prominent play-by-play announcer and studio host covering major events across multiple sports.
  • C. Jim Nantz
    Jim Nantz is a prominent American sportscaster best known for his long-running play-by-play coverage of major events such as the NFL, NCAA basketball, and The Masters on CBS.
  • D. Chris Berman
    Chris Berman is a longtime ESPN sportscaster best known for his energetic NFL coverage and signature catchphrases.
  • E. Dick Enberg
    Dick Enberg was a renowned American sportscaster celebrated for his versatile play-by-play work across major sports on network television for several decades.
  • 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_69d381be340c8190b05998703d42d224 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4e9e42da08190a5383df3df6d3c18 completed April 7, 2026, 11:26 a.m.
NED1 Entity disambiguation (via context triple) batch_69d7fbd13c888190b3a79a9aacb5291e completed April 9, 2026, 7:19 p.m.
Created at: April 6, 2026, 12:08 p.m.