Triple

T22980193
Position Surface form Disambiguated ID Type / Status
Subject Too Many Mornings E571436 entity
Predicate associatedWithCharacter P1481 FINISHED
Object Ben Stone NE NERFINISHED

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: Ben Stone | Statement: [Too Many Mornings, associatedWithCharacter, Ben Stone]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ben Stone
Context triple: [Too Many Mornings, associatedWithCharacter, Ben Stone]
  • A. Ben Stone chosen
    Ben Stone is a principled and hard-driving executive assistant district attorney who serves as one of the original lead prosecutors on the television series "Law & Order."
  • B. Ben Stone
    Ben Stone is the immature yet well-meaning slacker protagonist of the comedy film "Knocked Up," whose unexpected impending fatherhood forces him to confront adulthood and responsibility.
  • C. Peter Stone
    Peter Stone was an American screenwriter and playwright best known for crafting witty, sophisticated scripts for films such as "Charade" and the musical "1776."
  • D. Peter Stone
    Peter Stone is a comic book creator and entrepreneur best known as a co-founder of the independent publisher Continuity Comics.
  • E. Peter Stone
    Peter Stone is an American computer scientist known for his influential work in artificial intelligence and robotics, particularly in multiagent systems and robot soccer.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e245b3c50481908bb3741ec9f40862 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f18294c4c8819083ef85d9cb736613 completed April 29, 2026, 4:01 a.m.
Created at: April 17, 2026, 3:49 p.m.