Triple

T3584588
Position Surface form Disambiguated ID Type / Status
Subject Dan Laustsen E75880 entity
Predicate spouse P13 FINISHED
Object Tine Laustsen
Tine Laustsen is known as the spouse of acclaimed Danish cinematographer Dan Laustsen.
 E375131 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: Tine Laustsen | Statement: [Dan Laustsen, spouse, Tine Laustsen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tine Laustsen
Context triple: [Dan Laustsen, spouse, Tine Laustsen]
  • A. Peter Aalbæk Jensen
    Peter Aalbæk Jensen is a Danish film producer and co-founder of the influential production company Zentropa, known for his collaborations with prominent directors such as Lars von Trier.
  • B. Christian Møller
    Christian Møller was a Danish theoretical physicist known for his contributions to quantum electrodynamics and the theory of relativity.
  • C. Folmar Blangsted
    Folmar Blangsted was a Danish-born American film editor known for his work on major Hollywood productions in the mid-20th century.
  • D. Mikkel Svane
    Mikkel Svane is a Danish entrepreneur best known as the co-founder and longtime CEO of the customer service software company Zendesk.
  • E. Ingvard Eversen Nielsen
    Ingvard Eversen Nielsen was the father of Canadian-American actor and comedian Leslie Nielsen.
  • 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: Tine Laustsen
Triple: [Dan Laustsen, spouse, Tine Laustsen]
Generated description
Tine Laustsen is known as the spouse of acclaimed Danish cinematographer Dan Laustsen.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tine Laustsen
Target entity description: Tine Laustsen is known as the spouse of acclaimed Danish cinematographer Dan Laustsen.
  • A. Peter Aalbæk Jensen
    Peter Aalbæk Jensen is a Danish film producer and co-founder of the influential production company Zentropa, known for his collaborations with prominent directors such as Lars von Trier.
  • B. Christian Møller
    Christian Møller was a Danish theoretical physicist known for his contributions to quantum electrodynamics and the theory of relativity.
  • C. Folmar Blangsted
    Folmar Blangsted was a Danish-born American film editor known for his work on major Hollywood productions in the mid-20th century.
  • D. Mikkel Svane
    Mikkel Svane is a Danish entrepreneur best known as the co-founder and longtime CEO of the customer service software company Zendesk.
  • E. Ingvard Eversen Nielsen
    Ingvard Eversen Nielsen was the father of Canadian-American actor and comedian Leslie Nielsen.
  • 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_69ad85d6dc3c8190b491b79b83e25461 completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adc10f9b508190bde4a4e4711dd452 completed March 8, 2026, 6:33 p.m.
NED1 Entity disambiguation (via context triple) batch_69b44efa419481908f07a9367adc4e42 completed March 13, 2026, 5:52 p.m.
NEDg Description generation batch_69b4505350e4819085ef8cca532d9eda completed March 13, 2026, 5:58 p.m.
NED2 Entity disambiguation (via description) batch_69b450a915b081908889b4c5ea2d3024 completed March 13, 2026, 6 p.m.
Created at: March 8, 2026, 3:21 p.m.