Triple

T10661350
Position Surface form Disambiguated ID Type / Status
Subject Aalborg Airport E251232 entity
Predicate locatedIn P40 FINISHED
Object Nørresundby
Nørresundby is a town in northern Denmark situated across the Limfjord from Aalborg, forming part of the Aalborg metropolitan area.
E876560 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: Nørresundby | Statement: [Aalborg Airport, locatedIn, Nørresundby]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nørresundby
Context triple: [Aalborg Airport, locatedIn, Nørresundby]
  • A. Rødovre
    Rødovre is a suburban municipality in the Capital Region of Denmark, located just west of central Copenhagen.
  • B. Næstved
    Næstved is a historic market town and commercial center in southern Denmark, located on the island of Zealand.
  • C. Birkerød
    Birkerød is a suburban town in northeastern Zealand, Denmark, known for its residential character, green surroundings, and proximity to Copenhagen.
  • D. Nykøbing Mors
    Nykøbing Mors is a Danish coastal town on the island of Mors, known as its main urban center and a local hub for fishing, trade, and tourism.
  • E. Holbæk
    Holbæk is a coastal town and municipality in northwestern Zealand, Denmark, known for its harbor on Holbæk Fjord and role as a regional commercial and cultural center.
  • 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: Nørresundby
Triple: [Aalborg Airport, locatedIn, Nørresundby]
Generated description
Nørresundby is a town in northern Denmark situated across the Limfjord from Aalborg, forming part of the Aalborg metropolitan area.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Nørresundby
Target entity description: Nørresundby is a town in northern Denmark situated across the Limfjord from Aalborg, forming part of the Aalborg metropolitan area.
  • A. Rødovre
    Rødovre is a suburban municipality in the Capital Region of Denmark, located just west of central Copenhagen.
  • B. Næstved
    Næstved is a historic market town and commercial center in southern Denmark, located on the island of Zealand.
  • C. Birkerød
    Birkerød is a suburban town in northeastern Zealand, Denmark, known for its residential character, green surroundings, and proximity to Copenhagen.
  • D. Nykøbing Mors
    Nykøbing Mors is a Danish coastal town on the island of Mors, known as its main urban center and a local hub for fishing, trade, and tourism.
  • E. Holbæk
    Holbæk is a coastal town and municipality in northwestern Zealand, Denmark, known for its harbor on Holbæk Fjord and role as a regional commercial and cultural center.
  • 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_69d6aa5b0d2881909584b20efc5877f0 completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d6e017f97c8190b22765a6f1e6719d completed April 8, 2026, 11:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69d97a8cabc88190b430cb08ed0fc515 completed April 10, 2026, 10:32 p.m.
NEDg Description generation batch_69d97cd3eab48190a191f0d8278ef761 completed April 10, 2026, 10:42 p.m.
NED2 Entity disambiguation (via description) batch_69d97e189800819087bf6af15b2370a2 completed April 10, 2026, 10:47 p.m.
Created at: April 8, 2026, 9:08 p.m.