Triple

T10367448
Position Surface form Disambiguated ID Type / Status
Subject Human Traffic E244291 entity
Predicate mainCharacter P1183 FINISHED
Object Koop
Koop is a central character in the British cult film "Human Traffic," known for his role in the story’s depiction of youth and club culture.
 E860616 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: Koop | Statement: [Human Traffic, mainCharacter, Koop]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Koop
Context triple: [Human Traffic, mainCharacter, Koop]
  • A. Koop
    Koop is a surname most prominently associated with C. Everett Koop, the influential former Surgeon General of the United States.
  • B. Koops
    Koops is a timid Koopa Troopa character and party member from the video game "Paper Mario: The Thousand-Year Door."
  • C. Koopmans
    Koopmans is a Dutch surname most notably associated with Nobel Prize–winning economist Tjalling C. Koopmans.
  • D. Koopie Koo
    Koopie Koo is a minor character in the Paper Mario series, known as Koops' affectionate and supportive Koopa girlfriend from his hometown.
  • E. G Koop
    G Koop is a hip-hop record producer and musician known for his work on major rap hits, including Migos' breakout single "Bad and Boujee."
  • 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: Koop
Triple: [Human Traffic, mainCharacter, Koop]
Generated description
Koop is a central character in the British cult film "Human Traffic," known for his role in the story’s depiction of youth and club culture.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Koop
Target entity description: Koop is a central character in the British cult film "Human Traffic," known for his role in the story’s depiction of youth and club culture.
  • A. Koop
    Koop is a surname most prominently associated with C. Everett Koop, the influential former Surgeon General of the United States.
  • B. Koops
    Koops is a timid Koopa Troopa character and party member from the video game "Paper Mario: The Thousand-Year Door."
  • C. Coop
    Coop is a central character in the work "Divisadero," around whom much of the story’s emotional and narrative focus revolves.
  • D. Koopmans
    Koopmans is a Dutch surname most notably associated with Nobel Prize–winning economist Tjalling C. Koopmans.
  • E. Koopie Koo
    Koopie Koo is a minor character in the Paper Mario series, known as Koops' affectionate and supportive Koopa girlfriend from his hometown.
  • 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_69d381b3e328819094b23b8edcd29b5a completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4e96fd6f081908f630a16106996d9 completed April 7, 2026, 11:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69d79546b078819089dec7628c95a681 completed April 9, 2026, 12:02 p.m.
NEDg Description generation batch_69d7bdde34408190a047ede29b91e182 completed April 9, 2026, 2:55 p.m.
NED2 Entity disambiguation (via description) batch_69d7e5fc6a008190b2a2326840074b53 completed April 9, 2026, 5:46 p.m.
Created at: April 6, 2026, noon