Triple

T1575427
Position Surface form Disambiguated ID Type / Status
Subject Funen E33638 entity
Predicate hasCity P316 FINISHED
Object Nyborg
Nyborg is a historic coastal town and former royal seat in central Denmark, located on the island of Funen.
 E233013 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: Nyborg | Statement: [Funen, hasCity, Nyborg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Nyborg
Context triple: [Funen, hasCity, Nyborg]
  • A. Svendborg
    Svendborg is a historic coastal town and seaport in southern Denmark known for its maritime heritage and location on the island of Funen.
  • B. Kolding
    Kolding is a historic Danish city in Southern Jutland known for Koldinghus Castle, its fjord-side location, and its role as a regional cultural and educational center.
  • C. Viborg
    Viborg is the Swedish name for the historic Karelian city of Vyborg, located near the Finnish border on the Gulf of Finland.
  • D. Hellebæk
    Hellebæk is a coastal town in northeastern Zealand, Denmark, known for its scenic setting near Helsingør and its historic industrial and residential architecture.
  • E. Hjørring
    Hjørring is a historic town in northern Denmark known as one of the oldest settlements in the Vendsyssel region and a local 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: Nyborg
Triple: [Funen, hasCity, Nyborg]
Generated description
Nyborg is a historic coastal town and former royal seat in central Denmark, located on the island of Funen.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Nyborg
Target entity description: Nyborg is a historic coastal town and former royal seat in central Denmark, located on the island of Funen.
  • A. Svendborg
    Svendborg is a historic coastal town and seaport in southern Denmark known for its maritime heritage and location on the island of Funen.
  • B. Kolding
    Kolding is a historic Danish city in Southern Jutland known for Koldinghus Castle, its fjord-side location, and its role as a regional cultural and educational center.
  • C. Viborg
    Viborg is the Swedish name for the historic Karelian city of Vyborg, located near the Finnish border on the Gulf of Finland.
  • D. Hellebæk
    Hellebæk is a coastal town in northeastern Zealand, Denmark, known for its scenic setting near Helsingør and its historic industrial and residential architecture.
  • E. Hjørring
    Hjørring is a historic town in northern Denmark known as one of the oldest settlements in the Vendsyssel region and a local 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_69a885f27a4c8190a4622252cdf54c00 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a908d2cffc819090f3d5cbebae3307 completed March 5, 2026, 4:38 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae303a4f5881909746b1dae558f8b0 completed March 9, 2026, 2:28 a.m.
NEDg Description generation batch_69ae30e0411c81908cd19bf8d3525056 completed March 9, 2026, 2:30 a.m.
NED2 Entity disambiguation (via description) batch_69ae31849d7c8190a4e3c90334bf21a5 completed March 9, 2026, 2:33 a.m.
Created at: March 4, 2026, 7:27 p.m.