Triple

T16909850
Position Surface form Disambiguated ID Type / Status
Subject Louie De Palma E410165 entity
Predicate relationshipWith P10260 FINISHED
Object Tony Banta E975914 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 Banta | Statement: [Louie De Palma, relationshipWith, Tony Banta]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tony Banta
Context triple: [Louie De Palma, relationshipWith, Tony Banta]
  • A. Tony Banta chosen
    Tony Banta is a kind-hearted but somewhat dim-witted boxer and cab driver portrayed by Tony Danza on the classic sitcom "Taxi."
  • B. Gary Bonner
    Gary Bonner is a musician best known as a member of the new wave band Tom Tom Club.
  • C. Gary Tarpinian
    Gary Tarpinian was an American television producer best known for creating and producing popular nonfiction and reality series, particularly in the history and science genres.
  • D. Verne Brown
    Verne Brown is one of the time-traveling sons of Dr. Emmett Brown featured in the Back to the Future franchise.
  • E. Matt Bondurant
    Matt Bondurant is an American novelist and academic best known for his historical crime novel "The Wettest County in the World," which was adapted into the film "Lawless."
  • 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_69d886c7b1e481908c3766dfa8c13458 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3ca3bdc3081908a9b4f6e63405348 completed April 18, 2026, 6:15 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00c7b98d5c8190b61de47b246549e3 completed May 10, 2026, 6 p.m.
Created at: April 10, 2026, 5:30 a.m.