Triple

T7049487
Position Surface form Disambiguated ID Type / Status
Subject How to Steal a Million E163727 entity
Predicate mainCharacter P1183 FINISHED
Object Simon Dermott
Simon Dermott is the charming, clever art expert and thief portrayed by Peter O’Toole in the 1966 heist comedy film "How to Steal a Million."
E639393 NE FINISHED

How this triple was built (4 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: Simon Dermott | Statement: [How to Steal a Million, mainCharacter, Simon Dermott]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Simon Dermott
Context triple: [How to Steal a Million, mainCharacter, Simon Dermott]
  • A. David DeMott
    David DeMott is an American entrepreneur best known as one of the founders behind the creation of the SeaWorld marine theme park concept.
  • B. Phil Jordan
    Phil Jordan is a musician best known as a former member of the American rock band No Doubt.
  • C. Sean Whalen
    Sean Whalen is an American character actor known for his distinctive, often quirky roles in film and television, including horror and cult favorites.
  • D. Matthew Richardson
    Matthew Richardson is a former Australian rules footballer widely regarded as one of Richmond Football Club’s greatest forwards and a fan favourite in the AFL.
  • E. Kevin Stoney
    Kevin Stoney was a British character actor best known for his villainous roles in classic science fiction television, particularly in series like Doctor Who.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Simon Dermott
Triple: [How to Steal a Million, mainCharacter, Simon Dermott]
Generated description
Simon Dermott is the charming, clever art expert and thief portrayed by Peter O’Toole in the 1966 heist comedy film "How to Steal a Million."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Simon Dermott
Target entity description: Simon Dermott is the charming, clever art expert and thief portrayed by Peter O’Toole in the 1966 heist comedy film "How to Steal a Million."
  • A. David DeMott
    David DeMott is an American entrepreneur best known as one of the founders behind the creation of the SeaWorld marine theme park concept.
  • B. Phil Jordan
    Phil Jordan is a musician best known as a former member of the American rock band No Doubt.
  • C. Sean Whalen
    Sean Whalen is an American character actor known for his distinctive, often quirky roles in film and television, including horror and cult favorites.
  • D. Matthew Richardson
    Matthew Richardson is a former Australian rules footballer widely regarded as one of Richmond Football Club’s greatest forwards and a fan favourite in the AFL.
  • E. Kevin Stoney
    Kevin Stoney was a British character actor best known for his villainous roles in classic science fiction television, particularly in series like Doctor Who.
  • F. None of above. chosen

Provenance (5 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_69c6885f598c8190b6b6495c59d8d962 completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6e24d5e8c8190b37e56107e6da8ab completed March 27, 2026, 8:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7888bba9c8190b6414b56e5588ec0 completed March 28, 2026, 7:51 a.m.
NEDg Description generation batch_69c788f292a08190bf3543ecfc245d12 completed March 28, 2026, 7:53 a.m.
NED2 Entity disambiguation (via description) batch_69c789a6ea988190ad2db2442f0a5e8f completed March 28, 2026, 7:56 a.m.
Created at: March 27, 2026, 2:37 p.m.