Triple

T12183579
Position Surface form Disambiguated ID Type / Status
Subject Plaza Sésamo E290277 entity
Predicate hasCharacter P2308 FINISHED
Object Lola
Lola is a Muppet-style character from the Mexican adaptation of Sesame Street, Plaza Sésamo, known for engaging children through songs and educational segments.
 E967635 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: [Plaza Sésamo, hasCharacter, Lola]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lola
Context triple: [Plaza Sésamo, hasCharacter, 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: [Plaza Sésamo, hasCharacter, Lola]
Generated description
Lola is a Muppet-style character from the Mexican adaptation of Sesame Street, Plaza Sésamo, known for engaging children through songs and educational segments.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lola
Target entity description: Lola is a Muppet-style character from the Mexican adaptation of Sesame Street, Plaza Sésamo, known for engaging children through songs and educational segments.
  • A. Lola
    Lola is a fictional character portrayed by British actor Chiwetel Ejiofor.
  • B. Lola
    Lola is the charismatic drag queen and performer who serves as the central catalyst for change in the musical and film "Kinky Boots."
  • 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 the seductive, devilish femme fatale character in the musical "Damn Yankees," known for her show-stopping number "Whatever Lola Wants."
  • E. 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.
  • 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_69d6ab64de5881908d56eb7a75c6cc69 completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d915fd8dac8190928059ad2b6bbbf3 completed April 10, 2026, 3:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69f5f6aecb0881909084f3ff2a9e52ea completed May 2, 2026, 1:05 p.m.
NEDg Description generation batch_69f600b7e1788190b1df4fdfd96118d0 completed May 2, 2026, 1:48 p.m.
NED2 Entity disambiguation (via description) batch_69f604c4ef7c8190bc128b1aa535744d completed May 2, 2026, 2:05 p.m.
Created at: April 8, 2026, 9:50 p.m.