Triple

T7488600
Position Surface form Disambiguated ID Type / Status
Subject Little Belt bridges E176945 entity
Predicate maintainedBy P86 FINISHED
Object Banedanmark
Banedanmark is the Danish government agency responsible for owning, maintaining, and managing most of Denmark’s railway infrastructure.
 E669126 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: Banedanmark | Statement: [Little Belt bridges, maintainedBy, Banedanmark]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Banedanmark
Context triple: [Little Belt bridges, maintainedBy, Banedanmark]
  • A. Billund, Denmark
    Billund, Denmark is a small Danish town best known as the birthplace of LEGO and home to the original LEGOLAND theme park.
  • B. Denmark
    Denmark is a Nordic country in Northern Europe known for its high standard of living, strong welfare state, and role as a founding member of NATO and the United Nations.
  • C. Karup, Denmark
    Karup, Denmark is a village in central Jutland best known as a major military hub and home to the primary air base of the Royal Danish Air Force.
  • D. Okstindan
    Okstindan is a mountain range in northern Norway known for its rugged peaks and glaciers, including the prominent summit Oksskolten.
  • E. Rødenes
    Rødenes is a small village and former municipality in southeastern Norway, known for its rural landscape and historic church.
  • 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: Banedanmark
Triple: [Little Belt bridges, maintainedBy, Banedanmark]
Generated description
Banedanmark is the Danish government agency responsible for owning, maintaining, and managing most of Denmark’s railway infrastructure.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Banedanmark
Target entity description: Banedanmark is the Danish government agency responsible for owning, maintaining, and managing most of Denmark’s railway infrastructure.
  • A. Billund, Denmark
    Billund, Denmark is a small Danish town best known as the birthplace of LEGO and home to the original LEGOLAND theme park.
  • B. Denmark
    Denmark is a Nordic country in Northern Europe known for its high standard of living, strong welfare state, and role as a founding member of NATO and the United Nations.
  • C. Karup, Denmark
    Karup, Denmark is a village in central Jutland best known as a major military hub and home to the primary air base of the Royal Danish Air Force.
  • D. Okstindan
    Okstindan is a mountain range in northern Norway known for its rugged peaks and glaciers, including the prominent summit Oksskolten.
  • E. Rødenes
    Rødenes is a small village and former municipality in southeastern Norway, known for its rural landscape and historic church.
  • 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_69c6f55965ac81909d3c3a5422b22d44 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.