Triple

T16310563
Position Surface form Disambiguated ID Type / Status
Subject Big Mouth E396042 entity
Predicate featuresCharacter P626 FINISHED
Object Lola Skumpy
Lola Skumpy is a recurring character on the animated comedy series "Big Mouth," known as a loud, dramatic, and often abrasive middle school student.
 E1206330 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 Skumpy | Statement: [Big Mouth, featuresCharacter, Lola Skumpy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lola Skumpy
Context triple: [Big Mouth, featuresCharacter, Lola Skumpy]
  • 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 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 fictional character portrayed by British actor Chiwetel Ejiofor.
  • 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 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 Skumpy
Triple: [Big Mouth, featuresCharacter, Lola Skumpy]
Generated description
Lola Skumpy is a recurring character on the animated comedy series "Big Mouth," known as a loud, dramatic, and often abrasive middle school student.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lola Skumpy
Target entity description: Lola Skumpy is a recurring character on the animated comedy series "Big Mouth," known as a loud, dramatic, and often abrasive middle school student.
  • 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 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 fictional character portrayed by British actor Chiwetel Ejiofor.
  • 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 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

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_69d87f23bb088190a16fbb91a1957ea5 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e288da27f88190aa241e3addf9cd7f completed April 17, 2026, 7:24 p.m.
NED1 Entity disambiguation (via context triple) batch_6a001fa6ceb48190b937a15b94fd3cfa completed May 10, 2026, 6:03 a.m.
NEDg Description generation batch_6a00219696dc8190bcce66c1eeb07561 completed May 10, 2026, 6:11 a.m.
NED2 Entity disambiguation (via description) batch_6a002221fe7c819083c8ede5e63b0908 completed May 10, 2026, 6:13 a.m.
Created at: April 10, 2026, 5:06 a.m.