Triple

T3758034
Position Surface form Disambiguated ID Type / Status
Subject Good Vibes E82094 entity
Predicate hasMainCharacter P1183 FINISHED
Object Wadska
Wadska is a central character in the series "Good Vibes," known for his laid-back, comedic personality within the show's ensemble cast.
 E385720 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: Wadska | Statement: [Good Vibes, hasMainCharacter, Wadska]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Wadska
Context triple: [Good Vibes, hasMainCharacter, Wadska]
  • A. Marulan
    Marulan is a small town in New South Wales, Australia, known as a rural service centre located near the geographic midpoint between Sydney and Canberra.
  • B. Golian
    Golian is a Slovak surname most notably associated with Ján Golian, a key military leader of the Slovak National Uprising during World War II.
  • C. Tynaarlo
    Tynaarlo is a municipality in the northeastern Netherlands known for its rural character and location between the cities of Groningen and Assen.
  • D. Balice
    Balice is a village near Kraków in southern Poland, best known for hosting the region’s main international airport.
  • E. Ballimaran
    Ballimaran is a historic, densely packed lane in Old Delhi known for its traditional shops, old havelis, and association with the poet Mirza Ghalib.
  • 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: Wadska
Triple: [Good Vibes, hasMainCharacter, Wadska]
Generated description
Wadska is a central character in the series "Good Vibes," known for his laid-back, comedic personality within the show's ensemble cast.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Wadska
Target entity description: Wadska is a central character in the series "Good Vibes," known for his laid-back, comedic personality within the show's ensemble cast.
  • A. Marulan
    Marulan is a small town in New South Wales, Australia, known as a rural service centre located near the geographic midpoint between Sydney and Canberra.
  • B. Golian
    Golian is a Slovak surname most notably associated with Ján Golian, a key military leader of the Slovak National Uprising during World War II.
  • C. Tynaarlo
    Tynaarlo is a municipality in the northeastern Netherlands known for its rural character and location between the cities of Groningen and Assen.
  • D. Balice
    Balice is a village near Kraków in southern Poland, best known for hosting the region’s main international airport.
  • E. Ballimaran
    Ballimaran is a historic, densely packed lane in Old Delhi known for its traditional shops, old havelis, and association with the poet Mirza Ghalib.
  • 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_69ad8b1db40081908b61ffa6b78afd4d completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adcbc04d348190b0e4a90d18bdd160 completed March 8, 2026, 7:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4e50f77fc8190b7774a7359118c9c completed March 14, 2026, 4:33 a.m.
NEDg Description generation batch_69b4e5fe22f0819088effd8a0eae72e6 completed March 14, 2026, 4:37 a.m.
NED2 Entity disambiguation (via description) batch_69b4e671e02c819094cae2a3a2abb1b4 completed March 14, 2026, 4:39 a.m.
Created at: March 8, 2026, 3:35 p.m.