Triple

T14060421
Position Surface form Disambiguated ID Type / Status
Subject Verhagen E338328 entity
Predicate hasNotableBearer P458 FINISHED
Object Yvonne Verhagen
Yvonne Verhagen is a person notable enough to be recognized as a prominent bearer of the surname Verhagen.
E1089520 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: Yvonne Verhagen | Statement: [Verhagen, hasNotableBearer, Yvonne Verhagen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Yvonne Verhagen
Context triple: [Verhagen, hasNotableBearer, Yvonne Verhagen]
  • A. Catharina (Caro) Verbeek
    Catharina (Caro) Verbeek is a Dutch politician who serves as the mayor of the municipality of Bodegraven-Reeuwijk in the Netherlands.
  • B. Sari van Veenendaal
    Sari van Veenendaal is a Dutch professional football goalkeeper renowned for her performances with the Netherlands women’s national team and top European clubs.
  • C. Astrid Nienhuis
    Astrid Nienhuis is a Dutch politician who serves as the mayor of the municipality of Heemstede in the Netherlands.
  • D. Wendy van Dijk
    Wendy van Dijk is a Dutch television presenter and actress best known for hosting popular entertainment shows and playing comedic characters on Dutch TV.
  • E. Lotte Verbeek
    Lotte Verbeek is a Dutch actress and model best known internationally for her roles in historical drama series such as The Borgias and Outlander.
  • 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: Yvonne Verhagen
Triple: [Verhagen, hasNotableBearer, Yvonne Verhagen]
Generated description
Yvonne Verhagen is a person notable enough to be recognized as a prominent bearer of the surname Verhagen.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Yvonne Verhagen
Target entity description: Yvonne Verhagen is a person notable enough to be recognized as a prominent bearer of the surname Verhagen.
  • A. Catharina (Caro) Verbeek
    Catharina (Caro) Verbeek is a Dutch politician who serves as the mayor of the municipality of Bodegraven-Reeuwijk in the Netherlands.
  • B. Sari van Veenendaal
    Sari van Veenendaal is a Dutch professional football goalkeeper renowned for her performances with the Netherlands women’s national team and top European clubs.
  • C. Astrid Nienhuis
    Astrid Nienhuis is a Dutch politician who serves as the mayor of the municipality of Heemstede in the Netherlands.
  • D. Wendy van Dijk
    Wendy van Dijk is a Dutch television presenter and actress best known for hosting popular entertainment shows and playing comedic characters on Dutch TV.
  • E. Lotte Verbeek
    Lotte Verbeek is a Dutch actress and model best known internationally for her roles in historical drama series such as The Borgias and Outlander.
  • 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_69d81c67ba6c819091935650dfb3b895 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de5686f51c81908c33143ecbaae83d completed April 14, 2026, 3 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd324133f8819088c0d80a4e8fb5be completed May 8, 2026, 12:45 a.m.
NEDg Description generation batch_69fd339f04f48190abd13b7ce459c931 completed May 8, 2026, 12:51 a.m.
NED2 Entity disambiguation (via description) batch_69fd341b65e481908cd39e64e52583eb completed May 8, 2026, 12:53 a.m.
Created at: April 9, 2026, 10:21 p.m.