Triple

T7488801
Position Surface form Disambiguated ID Type / Status
Subject Danish–German border E176949 entity
Predicate nearCity P350 FINISHED
Object Kruså
Kruså is a small town in southern Denmark known for its location at a major road crossing on the Danish–German border.
 E669139 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: Kruså | Statement: [Danish–German border, nearCity, Kruså]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kruså
Context triple: [Danish–German border, nearCity, Kruså]
  • A. Ormåsen
    Ormåsen is a small residential village in Øvre Eiker municipality in Buskerud county, Norway.
  • B. Kvænangen
    Kvænangen is a fjord in northern Norway known for its dramatic coastal scenery, rich marine life, and traditional fishing communities.
  • C. Tjøme
    Tjøme is a scenic island and former municipality in Vestfold, Norway, known for its coastal landscapes, summer cabins, and popular seaside recreation areas.
  • D. Svaneke
    Svaneke is a picturesque coastal town on the Danish island of Bornholm, known for its well-preserved half-timbered houses, harbor, and traditional smokehouses.
  • E. Bekkestua
    Bekkestua is a suburban center in Bærum, Norway, functioning as a local commercial and transport hub just west of Oslo.
  • 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: Kruså
Triple: [Danish–German border, nearCity, Kruså]
Generated description
Kruså is a small town in southern Denmark known for its location at a major road crossing on the Danish–German border.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kruså
Target entity description: Kruså is a small town in southern Denmark known for its location at a major road crossing on the Danish–German border.
  • A. Ormåsen
    Ormåsen is a small residential village in Øvre Eiker municipality in Buskerud county, Norway.
  • B. Kvænangen
    Kvænangen is a fjord in northern Norway known for its dramatic coastal scenery, rich marine life, and traditional fishing communities.
  • C. Tjøme
    Tjøme is a scenic island and former municipality in Vestfold, Norway, known for its coastal landscapes, summer cabins, and popular seaside recreation areas.
  • D. Svaneke
    Svaneke is a picturesque coastal town on the Danish island of Bornholm, known for its well-preserved half-timbered houses, harbor, and traditional smokehouses.
  • E. Bekkestua
    Bekkestua is a suburban center in Bærum, Norway, functioning as a local commercial and transport hub just west of Oslo.
  • 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_69c69f24ac508190bb98fe927c0bd065 completed March 27, 2026, 3:15 p.m.
NER Named-entity recognition batch_69c6f55abcd481909e42ca857fe46cd1 completed March 27, 2026, 9:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69c83c71f5748190bdda4cf9b8dfc6ea completed March 28, 2026, 8:39 p.m.
NEDg Description generation batch_69c83e7b2ab08190a5ecb9b87af067a5 completed March 28, 2026, 8:47 p.m.
NED2 Entity disambiguation (via description) batch_69c842bad1e8819093bf61d9480dbd22 completed March 28, 2026, 9:06 p.m.
Created at: March 27, 2026, 3:43 p.m.