Triple

T17686835
Position Surface form Disambiguated ID Type / Status
Subject Hillerød Station E440913 entity
Predicate connectsTo P845 FINISHED
Object Helsinge
Helsinge is a town in North Zealand, Denmark, known as a local commercial and transport hub connected by rail to nearby cities including Hillerød.
 E1284564 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: Helsinge | Statement: [Hillerød Station, connectsTo, Helsinge]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Helsinge
Context triple: [Hillerød Station, connectsTo, Helsinge]
  • A. Copenhagen
    Copenhagen is the capital and largest city of Denmark, known for its historic architecture, vibrant cultural scene, and high quality of life.
  • B. Copenhagen
    Copenhagen is a popular American smokeless tobacco (chewing tobacco/dip) brand known for its long history and strong presence in the U.S. market.
  • C. Hankø
    Hankø is a small Norwegian island and resort area known for its sailing, summer tourism, and scenic coastal landscapes.
  • D. La Hague
    La Hague is a coastal commune in northwestern France known for its rugged cliffs, scenic landscapes, and proximity to major nuclear reprocessing facilities.
  • E. Esbjerg
    Esbjerg is a major Danish port city on the North Sea, known for its offshore oil and wind industry, maritime heritage, and role as a regional economic center in western Jutland.
  • 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: Helsinge
Triple: [Hillerød Station, connectsTo, Helsinge]
Generated description
Helsinge is a town in North Zealand, Denmark, known as a local commercial and transport hub connected by rail to nearby cities including Hillerød.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Helsinge
Target entity description: Helsinge is a town in North Zealand, Denmark, known as a local commercial and transport hub connected by rail to nearby cities including Hillerød.
  • A. Copenhagen
    Copenhagen is the capital and largest city of Denmark, known for its historic architecture, vibrant cultural scene, and high quality of life.
  • B. Copenhagen
    Copenhagen is a popular American smokeless tobacco (chewing tobacco/dip) brand known for its long history and strong presence in the U.S. market.
  • C. Hankø
    Hankø is a small Norwegian island and resort area known for its sailing, summer tourism, and scenic coastal landscapes.
  • D. La Hague
    La Hague is a coastal commune in northwestern France known for its rugged cliffs, scenic landscapes, and proximity to major nuclear reprocessing facilities.
  • E. Esbjerg
    Esbjerg is a major Danish port city on the North Sea, known for its offshore oil and wind industry, maritime heritage, and role as a regional economic center in western Jutland.
  • 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_69d8b9e940b081908b862bb0e6e89b0d completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e470488c4081909b747313ef97b69c completed April 19, 2026, 6:03 a.m.
NED1 Entity disambiguation (via context triple) batch_6a02301aab388190a6a44feb57468201 completed May 11, 2026, 7:38 p.m.
NEDg Description generation batch_6a02344201fc8190acc1783878117cdd completed May 11, 2026, 7:55 p.m.
NED2 Entity disambiguation (via description) batch_6a0234b09e688190af7c65fd89a2e4cd completed May 11, 2026, 7:57 p.m.
Created at: April 10, 2026, 10:03 a.m.