Triple

T9846170
Position Surface form Disambiguated ID Type / Status
Subject Roger Vadim E239346 entity
Predicate spouse P13 FINISHED
Object Annette Stroyberg
Annette Stroyberg was a Danish actress and model best known for her roles in European films of the late 1950s and 1960s.
 E866723 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: Annette Stroyberg | Statement: [Roger Vadim, spouse, Annette Stroyberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Annette Stroyberg
Context triple: [Roger Vadim, spouse, Annette Stroyberg]
  • A. Suzanne Verdal
    Suzanne Verdal is a Canadian woman best known as the real-life muse who inspired Leonard Cohen’s song “Suzanne.”
  • B. Karin Welge
    Karin Welge is a German politician who serves as the mayor of the city of Gelsenkirchen in North Rhine-Westphalia.
  • C. Sonja Haraldsen
    Sonja Haraldsen, now Queen Sonja of Norway, is the queen consort of King Harald V and a prominent member of the Norwegian royal family known for her cultural and charitable work.
  • D. Rena Lundigan
    Rena Lundigan is best known as the wife of American film and television actor William Lundigan.
  • E. Barbara Enberg
    Barbara Enberg is known as the wife of the late American sportscaster Dick Enberg.
  • 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: Annette Stroyberg
Triple: [Roger Vadim, spouse, Annette Stroyberg]
Generated description
Annette Stroyberg was a Danish actress and model best known for her roles in European films of the late 1950s and 1960s.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Annette Stroyberg
Target entity description: Annette Stroyberg was a Danish actress and model best known for her roles in European films of the late 1950s and 1960s.
  • A. Suzanne Verdal
    Suzanne Verdal is a Canadian woman best known as the real-life muse who inspired Leonard Cohen’s song “Suzanne.”
  • B. Karin Welge
    Karin Welge is a German politician who serves as the mayor of the city of Gelsenkirchen in North Rhine-Westphalia.
  • C. Sonja Haraldsen
    Sonja Haraldsen, now Queen Sonja of Norway, is the queen consort of King Harald V and a prominent member of the Norwegian royal family known for her cultural and charitable work.
  • D. Rena Lundigan
    Rena Lundigan is best known as the wife of American film and television actor William Lundigan.
  • E. Barbara Enberg
    Barbara Enberg is known as the wife of the late American sportscaster Dick Enberg.
  • 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_69ca84e3f0c48190ada72a65ebd50efd completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cdb36156308190b26892702f3b41e0 completed April 2, 2026, 12:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69d8dbc697388190b384c7ed9e6a65dc completed April 10, 2026, 11:15 a.m.
NEDg Description generation batch_69d8e8c683608190aa4333ed38e79f53 completed April 10, 2026, 12:10 p.m.
NED2 Entity disambiguation (via description) batch_69d901c7684c8190837ed9ef0c2428af completed April 10, 2026, 1:57 p.m.
Created at: March 30, 2026, 8:34 p.m.