Triple

T4168080
Position Surface form Disambiguated ID Type / Status
Subject Kinky Boots E84491 entity
Predicate leadCharacter P1668 FINISHED
Object Lola
Lola is the charismatic drag queen and performer who serves as the central catalyst for change in the musical and film "Kinky Boots."
 E416737 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: Lola | Statement: [Kinky Boots, leadCharacter, Lola]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lola
Context triple: [Kinky Boots, leadCharacter, Lola]
  • A. Lola
    Lola is a 1981 West German drama film directed by Rainer Werner Fassbinder, in which Armin Mueller-Stahl plays a prominent role in a story set in postwar Germany.
  • B. Lola
    Lola is a fictional character portrayed by British actor Chiwetel Ejiofor.
  • C. Carla
    Carla is a feminine given name commonly used in various languages, often considered the female form of Carl or Charles.
  • D. Lila
    Lila is a central female character in Max Frisch’s novel "Mein Name sei Gantenbein," around whom the narrator constructs one of his imagined lives and relationships.
  • E. Lela
    Lela is a feminine given name used in various cultures, often as a variant of Leila or Layla.
  • 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: Lola
Triple: [Kinky Boots, leadCharacter, Lola]
Generated description
Lola is the charismatic drag queen and performer who serves as the central catalyst for change in the musical and film "Kinky Boots."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lola
Target entity description: Lola is the charismatic drag queen and performer who serves as the central catalyst for change in the musical and film "Kinky Boots."
  • A. Lola
    Lola is a fictional character portrayed by British actor Chiwetel Ejiofor.
  • B. Lola
    Lola is a 1981 West German drama film directed by Rainer Werner Fassbinder, in which Armin Mueller-Stahl plays a prominent role in a story set in postwar Germany.
  • C. Carla
    Carla is a feminine given name commonly used in various languages, often considered the female form of Carl or Charles.
  • D. Lila
    Lila is a central female character in Max Frisch’s novel "Mein Name sei Gantenbein," around whom the narrator constructs one of his imagined lives and relationships.
  • E. Lela
    Lela is a feminine given name used in various cultures, often as a variant of Leila or Layla.
  • 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_69aed932cab48190b80ffe35f7029ae1 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69af02c573788190a60ab3f83b07a6f6 completed March 9, 2026, 5:26 p.m.
NED1 Entity disambiguation (via context triple) batch_69b57f4c2c988190959496cc0cc31cac completed March 14, 2026, 3:31 p.m.
NEDg Description generation batch_69b57fe89ed0819089d7e56568755b1c completed March 14, 2026, 3:34 p.m.
NED2 Entity disambiguation (via description) batch_69b5805cb7e88190b2f6ed6a18de9319 completed March 14, 2026, 3:35 p.m.
Created at: March 9, 2026, 3:44 p.m.