Triple

T10367808
Position Surface form Disambiguated ID Type / Status
Subject Human Traffic E244299 entity
Predicate mainCharacter P1183 FINISHED
Object Lulu
Lulu is a central character in the 1999 British cult film "Human Traffic," which explores the lives and clubbing culture of young people in Cardiff.
 E861459 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: Lulu | Statement: [Human Traffic, mainCharacter, Lulu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lulu
Context triple: [Human Traffic, mainCharacter, Lulu]
  • A. Lulu
    Lulu is a common feminine given name or nickname, often used as a diminutive form of names like Louise.
  • B. Lulu Bett
    Lulu Bett is the central character of Zona Gale's Pulitzer Prize-winning novel "Miss Lulu Bett," a quiet, self-effacing Midwestern woman whose constrained life and unexpected marriage spark a journey toward independence and self-realization.
  • C. Lillete
    Lillete is an alcoholic beverage brand that forms part of Pernod Ricard’s global spirits and drinks portfolio.
  • D. Lilli
    Lilli is a feminine given name, often used in German-speaking and other European countries, and famously borne by the actress Lilli Palmer.
  • E. Lulu Ferocity
    Lulu Ferocity is a central character known for her bold, dynamic presence and fierce, fashion-forward persona in the narrative of "Pose."
  • 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: Lulu
Triple: [Human Traffic, mainCharacter, Lulu]
Generated description
Lulu is a central character in the 1999 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: Lulu
Target entity description: Lulu is a central character in the 1999 British cult film "Human Traffic," which explores the lives and clubbing culture of young people in Cardiff.
  • A. Lulu
    Lulu is a common feminine given name or nickname, often used as a diminutive form of names like Louise.
  • B. Lulu Bett
    Lulu Bett is the central character of Zona Gale's Pulitzer Prize-winning novel "Miss Lulu Bett," a quiet, self-effacing Midwestern woman whose constrained life and unexpected marriage spark a journey toward independence and self-realization.
  • C. Lillete
    Lillete is an alcoholic beverage brand that forms part of Pernod Ricard’s global spirits and drinks portfolio.
  • D. Lilli
    Lilli is a feminine given name, often used in German-speaking and other European countries, and famously borne by the actress Lilli Palmer.
  • E. Lulu Ferocity
    Lulu Ferocity is a central character known for her bold, dynamic presence and fierce, fashion-forward persona in the narrative of "Pose."
  • 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_69d4e97106448190a075948e63184f47 completed April 7, 2026, 11:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69d7fb8e96e081908282bb0f82719abe completed April 9, 2026, 7:18 p.m.
NEDg Description generation batch_69d822d303888190aa556287b3b1cc03 completed April 9, 2026, 10:06 p.m.
NED2 Entity disambiguation (via description) batch_69d859b05a3881908c97cb173d160e44 completed April 10, 2026, 2 a.m.
Created at: April 6, 2026, noon