Triple

T20177498
Position Surface form Disambiguated ID Type / Status
Subject Bam Margera E492638 entity
Predicate familyName P18 FINISHED
Object Margera
Margera is a surname most prominently associated with American skateboarder and television personality Bam Margera.
 E1416690 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: Margera | Statement: [Bam Margera, familyName, Margera]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Margera
Context triple: [Bam Margera, familyName, Margera]
  • A. Fargas
    Fargas is a surname most notably associated with American actor Antonio Fargas, known for his character roles in film and television.
  • B. Debourg
    Debourg is a tram terminus and transport hub in Lyon, France, serving as one end of the city’s T1 tram line.
  • C. Marsella
    Marsella is a small Colombian town known for its traditional architecture and coffee-growing culture in the Andean region.
  • D. Reville
    Reville is an English surname most notably associated with Alma Reville, a film editor and screenwriter who collaborated closely with her husband, director Alfred Hitchcock.
  • E. Baulmes
    Baulmes is a Swiss village and municipality in the canton of Vaud, situated near the Jura Mountains and known for its scenic rural landscape.
  • 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: Margera
Triple: [Bam Margera, familyName, Margera]
Generated description
Margera is a surname most prominently associated with American skateboarder and television personality Bam Margera.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Margera
Target entity description: Margera is a surname most prominently associated with American skateboarder and television personality Bam Margera.
  • A. Fargas
    Fargas is a surname most notably associated with American actor Antonio Fargas, known for his character roles in film and television.
  • B. Debourg
    Debourg is a tram terminus and transport hub in Lyon, France, serving as one end of the city’s T1 tram line.
  • C. Marsella
    Marsella is a small Colombian town known for its traditional architecture and coffee-growing culture in the Andean region.
  • D. Reville
    Reville is an English surname most notably associated with Alma Reville, a film editor and screenwriter who collaborated closely with her husband, director Alfred Hitchcock.
  • E. Baulmes
    Baulmes is a Swiss village and municipality in the canton of Vaud, situated near the Jura Mountains and known for its scenic rural landscape.
  • 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_69da6268a034819081cbd9ea5a1c9475 completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e668ec4d7c81909fa4bdc58ed54aeb completed April 20, 2026, 5:57 p.m.
NED1 Entity disambiguation (via context triple) batch_6a083c79d2948190a1df0d3bd1f9c6ad completed May 16, 2026, 9:44 a.m.
NEDg Description generation batch_6a083d86085c8190a71d0dce65c659a4 completed May 16, 2026, 9:48 a.m.
NED2 Entity disambiguation (via description) batch_6a083eb380f48190bda8549d122f830b completed May 16, 2026, 9:53 a.m.
Created at: April 11, 2026, 11:36 p.m.