Triple

T11386743
Position Surface form Disambiguated ID Type / Status
Subject Wacky Races E269731 entity
Predicate hasCharacter P2308 FINISHED
Object Penelope Pitstop
Penelope Pitstop is a pink-clad, Southern-belle race car driver and damsel-in-distress heroine from Hanna-Barbera’s classic cartoon series.
 E923043 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: Penelope Pitstop | Statement: [Wacky Races, hasCharacter, Penelope Pitstop]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Penelope Pitstop
Context triple: [Wacky Races, hasCharacter, Penelope Pitstop]
  • A. Penelope Taynt
    Penelope Taynt is a fictional, obsessively devoted fan character and comedic stalker of Amanda Bynes on the Nickelodeon sketch comedy series "The Amanda Show."
  • B. Lola
    Lola is a fictional character portrayed by British actor Chiwetel Ejiofor.
  • 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 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
  • 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: Penelope Pitstop
Triple: [Wacky Races, hasCharacter, Penelope Pitstop]
Generated description
Penelope Pitstop is a pink-clad, Southern-belle race car driver and damsel-in-distress heroine from Hanna-Barbera’s classic cartoon series.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Penelope Pitstop
Target entity description: Penelope Pitstop is a pink-clad, Southern-belle race car driver and damsel-in-distress heroine from Hanna-Barbera’s classic cartoon series.
  • A. Penelope Taynt
    Penelope Taynt is a fictional, obsessively devoted fan character and comedic stalker of Amanda Bynes on the Nickelodeon sketch comedy series "The Amanda Show."
  • 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 the seductive, devilish femme fatale character in the musical "Damn Yankees," known for her show-stopping number "Whatever Lola Wants."
  • D. Lola
    Lola is a fictional character portrayed by British actor Chiwetel Ejiofor.
  • E. Lola
    Lola is the charismatic drag queen and performer who serves as the central catalyst for change in the musical and film "Kinky Boots."
  • 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_69d6aacdbc6c8190af6dc3d5f5d22836 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7fc378d808190b587a044ede67e1e completed April 9, 2026, 7:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69e58c3c9a7081908002d726ec9e7715 completed April 20, 2026, 2:15 a.m.
NEDg Description generation batch_69e5932d3cb88190807acdcdc3aaa9fc completed April 20, 2026, 2:45 a.m.
NED2 Entity disambiguation (via description) batch_69e59a0ab7e081908cb8761c4f82c664 completed April 20, 2026, 3:14 a.m.
Created at: April 8, 2026, 9:34 p.m.