Triple

T400836
Position Surface form Disambiguated ID Type / Status
Subject Halden E9275 entity
Predicate hasTwinTown P919 FINISHED
Object Herning
Herning is a Danish city in the Central Jutland region known for its trade fairs, conference facilities, and vibrant cultural and sports events.
 E51183 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: Herning | Statement: [Halden, hasTwinTown, Herning]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Herning
Context triple: [Halden, hasTwinTown, Herning]
  • 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. Arendal
    Arendal is a coastal town and municipality in southern Norway known historically as a regional political and trading center.
  • C. Hamburg
    Hamburg is Germany’s second-largest city and a major northern European port and cultural center on the River Elbe.
  • D. Funen
    Funen is Denmark’s third-largest island, located between the Jutland Peninsula and Zealand and known for its rolling countryside and the city of Odense, birthplace of Hans Christian Andersen.
  • E. Gothenburg
    Gothenburg is Sweden’s second-largest city, a major port on the country’s west coast known for its maritime heritage, universities, and vibrant cultural scene.
  • 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: Herning
Triple: [Halden, hasTwinTown, Herning]
Generated description
Herning is a Danish city in the Central Jutland region known for its trade fairs, conference facilities, and vibrant cultural and sports events.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Herning
Target entity description: Herning is a Danish city in the Central Jutland region known for its trade fairs, conference facilities, and vibrant cultural and sports events.
  • 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. Arendal
    Arendal is a coastal town and municipality in southern Norway known historically as a regional political and trading center.
  • C. Hamburg
    Hamburg is Germany’s second-largest city and a major northern European port and cultural center on the River Elbe.
  • D. Funen
    Funen is Denmark’s third-largest island, located between the Jutland Peninsula and Zealand and known for its rolling countryside and the city of Odense, birthplace of Hans Christian Andersen.
  • E. Gothenburg
    Gothenburg is Sweden’s second-largest city, a major port on the country’s west coast known for its maritime heritage, universities, and vibrant cultural scene.
  • 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_69a2e8004cb88190b92ed1add6abf41a completed Feb. 28, 2026, 1:05 p.m.
NER Named-entity recognition batch_69a2ec8e655c819081eff85c0ef55fa5 completed Feb. 28, 2026, 1:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69a413f275ac81908b6fd095a6d5a415 completed March 1, 2026, 10:24 a.m.
NEDg Description generation batch_69a41464d2a8819085ee2fc8a86a7628 completed March 1, 2026, 10:26 a.m.
NED2 Entity disambiguation (via description) batch_69a414a7aff08190ab54f4118cec790d completed March 1, 2026, 10:27 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.