Triple

T18797049
Position Surface form Disambiguated ID Type / Status
Subject Vendsyssel E459660 entity
Predicate contains P35 FINISHED
Object Rubjerg Knude
Rubjerg Knude is a famous coastal sand dune and former lighthouse site on the North Sea coast of northern Jutland in Denmark, known for its dramatic erosion and shifting sands.
 E1346338 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: Rubjerg Knude | Statement: [Vendsyssel, contains, Rubjerg Knude]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Rubjerg Knude
Context triple: [Vendsyssel, contains, Rubjerg Knude]
  • A. Møllehøj
    Møllehøj is the highest natural point in Denmark, located in the hilly region of eastern Jutland.
  • B. Bragernes
    Bragernes is a historic former town and district that now forms the northern part of the city of Drammen in Norway.
  • C. Egeskov
    Egeskov is a village on the island of Funen in Denmark best known for the nearby Renaissance water castle Egeskov Castle, one of Europe’s best-preserved moated castles.
  • D. Nakskov
    Nakskov is a historic port town in southern Denmark located on the island of Lolland, known for its maritime industry and coastal setting.
  • E. Birkelunden
    Birkelunden is a popular public park in Oslo’s Grünerløkka district, known for its green spaces, cultural events, and historic surroundings.
  • 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: Rubjerg Knude
Triple: [Vendsyssel, contains, Rubjerg Knude]
Generated description
Rubjerg Knude is a famous coastal sand dune and former lighthouse site on the North Sea coast of northern Jutland in Denmark, known for its dramatic erosion and shifting sands.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Rubjerg Knude
Target entity description: Rubjerg Knude is a famous coastal sand dune and former lighthouse site on the North Sea coast of northern Jutland in Denmark, known for its dramatic erosion and shifting sands.
  • A. Møllehøj
    Møllehøj is the highest natural point in Denmark, located in the hilly region of eastern Jutland.
  • B. Bragernes
    Bragernes is a historic former town and district that now forms the northern part of the city of Drammen in Norway.
  • C. Egeskov
    Egeskov is a village on the island of Funen in Denmark best known for the nearby Renaissance water castle Egeskov Castle, one of Europe’s best-preserved moated castles.
  • D. Nakskov
    Nakskov is a historic port town in southern Denmark located on the island of Lolland, known for its maritime industry and coastal setting.
  • E. Birkelunden
    Birkelunden is a popular public park in Oslo’s Grünerløkka district, known for its green spaces, cultural events, and historic surroundings.
  • 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_69d8d398c7d4819091cb2f7e48948aeb completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e5a020821881909749f6a1c6cd195b completed April 20, 2026, 3:40 a.m.
NED1 Entity disambiguation (via context triple) batch_6a05675ff6e88190abfa88e1085873f4 completed May 14, 2026, 6:10 a.m.
NEDg Description generation batch_6a05709c4e388190a451b7b5d6195934 completed May 14, 2026, 6:50 a.m.
NED2 Entity disambiguation (via description) batch_6a05716e0e888190a1e41d5cb2f860bd completed May 14, 2026, 6:53 a.m.
Created at: April 10, 2026, 11:53 a.m.