Triple

T18334459
Position Surface form Disambiguated ID Type / Status
Subject Richard Tyson E439233 entity
Predicate role P268 FINISHED
Object Cullen Crisp
Cullen Crisp is the ruthless drug-dealing villain in the action-comedy film "Kindergarten Cop."
 E525731 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: Cullen Crisp | Statement: [Richard Tyson, role, Cullen Crisp]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Cullen Crisp
Context triple: [Richard Tyson, role, Cullen Crisp]
  • A. Clete Boyer
    Clete Boyer was an American Major League Baseball third baseman, best known for his stellar defense with the New York Yankees during the 1960s.
  • B. Glen Tullman
    Glen Tullman is an American healthcare technology entrepreneur and executive best known for leading and building major digital health companies, including Allscripts.
  • C. John McIntire
    John McIntire was an American character actor known for his distinctive deep voice and roles in Western films and television, as well as voice work in classic Disney animated features.
  • D. J. T. Walsh
    J. T. Walsh was an American character actor known for his intense, often villainous roles in numerous films of the 1980s and 1990s.
  • E. Cliff Olin
    Cliff Olin is an American actor and writer, known for his work in film and television and as the son of actor-director Ken Olin.
  • 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: Cullen Crisp
Triple: [Richard Tyson, role, Cullen Crisp]
Generated description
Cullen Crisp is the ruthless drug-dealing villain in the action-comedy film "Kindergarten Cop."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Cullen Crisp
Target entity description: Cullen Crisp is the ruthless drug-dealing villain in the action-comedy film "Kindergarten Cop."
  • A. Clete Boyer
    Clete Boyer was an American Major League Baseball third baseman, best known for his stellar defense with the New York Yankees during the 1960s.
  • B. Glen Tullman
    Glen Tullman is an American healthcare technology entrepreneur and executive best known for leading and building major digital health companies, including Allscripts.
  • C. John McIntire
    John McIntire was an American character actor known for his distinctive deep voice and roles in Western films and television, as well as voice work in classic Disney animated features.
  • D. J. T. Walsh chosen
    J. T. Walsh was an American character actor known for his intense, often villainous roles in numerous films of the 1980s and 1990s.
  • E. Cliff Olin
    Cliff Olin is an American actor and writer, known for his work in film and television and as the son of actor-director Ken Olin.
  • F. None of above.

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_69d8b9175fec8190af865699b4e64d8c completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e50ecc91148190aa820fcd466009ce completed April 19, 2026, 5:20 p.m.
NED1 Entity disambiguation (via context triple) batch_6a043f1441ac8190af8ad50ea50c31ec completed May 13, 2026, 9:06 a.m.
NEDg Description generation batch_6a0440c2b1788190a61117c008e8e2a3 completed May 13, 2026, 9:13 a.m.
NED2 Entity disambiguation (via description) batch_6a04412ce3d481909044236be107de8d completed May 13, 2026, 9:15 a.m.
Created at: April 10, 2026, 10:36 a.m.