Triple

T10954809
Position Surface form Disambiguated ID Type / Status
Subject Damn Yankees E258816 entity
Predicate character P662 FINISHED
Object Lola
Lola is the seductive, devilish femme fatale character in the musical "Damn Yankees," known for her show-stopping number "Whatever Lola Wants."
E894972 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: [Damn Yankees, character, Lola]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lola
Context triple: [Damn Yankees, character, 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 the charismatic drag queen and performer who serves as the central catalyst for change in the musical and film "Kinky Boots."
  • D. 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.
  • E. 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.
  • 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: [Damn Yankees, character, Lola]
Generated description
Lola is the seductive, devilish femme fatale character in the musical "Damn Yankees," known for her show-stopping number "Whatever Lola Wants."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Lola
Target entity description: Lola is the seductive, devilish femme fatale character in the musical "Damn Yankees," known for her show-stopping number "Whatever Lola Wants."
  • A. Lola
    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 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.
  • C. 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.
  • D. Lola
    Lola is a 1961 French New Wave film directed by Jacques Demy, featuring Corinne Marchand in the title role as a cabaret singer in the port city of Nantes.
  • E. Lola
    Lola is a fictional character portrayed by British actor Chiwetel Ejiofor.
  • 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_69d6aa88500c819097d7032ca578e74f completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d770ff718c81909d4baebea3b56b83 completed April 9, 2026, 9:27 a.m.
NED1 Entity disambiguation (via context triple) batch_69e23c72196c8190b2336a130b64ab8e completed April 17, 2026, 1:58 p.m.
NEDg Description generation batch_69e24543bd2c8190a3c807baa76c30f6 completed April 17, 2026, 2:35 p.m.
NED2 Entity disambiguation (via description) batch_69e25d11ef24819091e730ae2416a058 completed April 17, 2026, 4:17 p.m.
Created at: April 8, 2026, 9:23 p.m.