Triple

T17171202
Position Surface form Disambiguated ID Type / Status
Subject Charlie Price E416736 entity
Predicate businessPartner P282 FINISHED
Object Lola
Lola is a glamorous and charismatic drag performer who becomes Charlie Price’s creative partner in revitalizing his struggling shoe factory in the musical "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: [Charlie Price, businessPartner, Lola]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lola
Context triple: [Charlie Price, businessPartner, Lola]
  • 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. Lola
    "Lola" is a 1970 rock song by The Kinks, famous for its catchy melody and narrative about a romantic encounter that plays with themes of gender identity and ambiguity.
  • D. Lola
    Lola is a lethal, acrobatic henchwoman and primary antagonist in the action film "Transporter 2," known for her distinctive red attire and high-impact fight scenes.
  • E. Lola
    Lola is the seductive, devilish femme fatale character in the musical "Damn Yankees," known for her show-stopping number "Whatever Lola Wants."
  • 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: [Charlie Price, businessPartner, Lola]
Generated description
Lola is a glamorous and charismatic drag performer who becomes Charlie Price’s creative partner in revitalizing his struggling shoe factory in the musical "Kinky Boots."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Lola
Target entity description: Lola is a glamorous and charismatic drag performer who becomes Charlie Price’s creative partner in revitalizing his struggling shoe factory in the musical "Kinky Boots."
  • A. Lola chosen
    Lola is the charismatic drag queen and performer who serves as the central catalyst for change in the musical and film "Kinky Boots."
  • B. Lola
    Lola is the seductive, devilish femme fatale character in the musical "Damn Yankees," known for her show-stopping number "Whatever Lola Wants."
  • C. Lola
    Lola is a character featured in the musical "The Origin of Love," likely serving as a significant figure within its narrative about love and identity.
  • D. Lola
    Lola is a character portrayed by actress and filmmaker Alice Englert.
  • E. Lola
    Lola is a fictional character portrayed by British actor Chiwetel Ejiofor.
  • 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_69d886d5f34c8190b24564dfaa63f3fb completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3fc097950819095631ee5679e03af completed April 18, 2026, 9:47 p.m.
NED1 Entity disambiguation (via context triple) batch_6a01483f85648190acaeb197013e1f1b completed May 11, 2026, 3:08 a.m.
NEDg Description generation batch_6a014a1993a48190bf65e590ff57c9c2 completed May 11, 2026, 3:16 a.m.
NED2 Entity disambiguation (via description) batch_6a014a7fa5208190a0a60649fe6292d1 completed May 11, 2026, 3:18 a.m.
Created at: April 10, 2026, 5:37 a.m.