Triple

T10367449
Position Surface form Disambiguated ID Type / Status
Subject Human Traffic E244291 entity
Predicate mainCharacter P1183 FINISHED
Object Nina
Nina is a central character in the British cult film "Human Traffic," which explores the lives and clubbing culture of young people in Cardiff.
 E863633 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: Nina | Statement: [Human Traffic, mainCharacter, Nina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Nina
Context triple: [Human Traffic, mainCharacter, Nina]
  • A. Nina
    Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
  • B. Nina
    Nina is a feminine given name used in various cultures, often as a short form of names like Antonina or Giannina, and borne by numerous notable figures in the arts and public life.
  • C. Nita
    Nita is a feminine given name commonly used as a shortened or affectionate form of longer names such as Juanita.
  • D. Nadya
    Nadya is a feminine given name, often used as a diminutive of Nadezhda in Slavic cultures.
  • E. Nina Romina
    Nina Romina is a ruthless local TV news director in the film "Nightcrawler," known for her willingness to exploit violent crime footage to boost ratings.
  • 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: Nina
Triple: [Human Traffic, mainCharacter, Nina]
Generated description
Nina is a central character in the British cult film "Human Traffic," which explores the lives and clubbing culture of young people in Cardiff.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Nina
Target entity description: Nina is a central character in the British cult film "Human Traffic," which explores the lives and clubbing culture of young people in Cardiff.
  • A. Nina
    Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
  • B. Nina
    Nina is a feminine given name used in various cultures, often as a short form of names like Antonina or Giannina, and borne by numerous notable figures in the arts and public life.
  • C. Nita
    Nita is a feminine given name commonly used as a shortened or affectionate form of longer names such as Juanita.
  • D. Nadya
    Nadya is a feminine given name, often used as a diminutive of Nadezhda in Slavic cultures.
  • E. Nina Romina
    Nina Romina is a ruthless local TV news director in the film "Nightcrawler," known for her willingness to exploit violent crime footage to boost ratings.
  • 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_69d87e64109881908c42a4fbcfd057be completed April 10, 2026, 4:36 a.m.
NEDg Description generation batch_69d886c325c4819089dac35eb26e7961 completed April 10, 2026, 5:12 a.m.
NED2 Entity disambiguation (via description) batch_69d88dbbe97c8190861e08f3ff39f91b completed April 10, 2026, 5:42 a.m.
Created at: April 6, 2026, noon