Triple

T1333897
Position Surface form Disambiguated ID Type / Status
Subject Ahaus E28702 entity
Predicate hasTwinTown P919 FINISHED
Object Haaksbergen
Haaksbergen is a town in the eastern Netherlands, near the German border, known for its rural surroundings and cross-border ties with neighboring German communities.
E261296 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: Haaksbergen | Statement: [Ahaus, hasTwinTown, Haaksbergen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Haaksbergen
Context triple: [Ahaus, hasTwinTown, Haaksbergen]
  • A. Uithoorn
    Uithoorn is a town and municipality in the province of North Holland in the Netherlands, situated along the Amstel River.
  • B. Hardinxveld-Giessendam
    Hardinxveld-Giessendam is a Dutch town and municipality known for its shipbuilding industry and location along the river Merwede in the province of South Holland.
  • C. Nieuwenhoorn
    Nieuwenhoorn is a village in the western Netherlands that forms part of the province of South Holland.
  • D. Heemstede
    Heemstede is a town and municipality in the province of North Holland in the Netherlands, known as a leafy residential suburb near Haarlem.
  • E. Nieuwendijk
    Nieuwendijk is one of Amsterdam’s oldest and busiest shopping streets, running through the historic city center near Dam Square.
  • 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: Haaksbergen
Triple: [Ahaus, hasTwinTown, Haaksbergen]
Generated description
Haaksbergen is a town in the eastern Netherlands, near the German border, known for its rural surroundings and cross-border ties with neighboring German communities.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Haaksbergen
Target entity description: Haaksbergen is a town in the eastern Netherlands, near the German border, known for its rural surroundings and cross-border ties with neighboring German communities.
  • A. Uithoorn
    Uithoorn is a town and municipality in the province of North Holland in the Netherlands, situated along the Amstel River.
  • B. Hardinxveld-Giessendam
    Hardinxveld-Giessendam is a Dutch town and municipality known for its shipbuilding industry and location along the river Merwede in the province of South Holland.
  • C. Nieuwenhoorn
    Nieuwenhoorn is a village in the western Netherlands that forms part of the province of South Holland.
  • D. Heemstede
    Heemstede is a town and municipality in the province of North Holland in the Netherlands, known as a leafy residential suburb near Haarlem.
  • E. Nieuwendijk
    Nieuwendijk is one of Amsterdam’s oldest and busiest shopping streets, running through the historic city center near Dam Square.
  • 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_69a498561a508190a3e1bc137c2b866a completed March 1, 2026, 7:49 p.m.
NER Named-entity recognition batch_69a4c1e98900819092c54c0fb58b958a completed March 1, 2026, 10:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69aea82191408190892e9a8e12504a8f completed March 9, 2026, 10:59 a.m.
NEDg Description generation batch_69aeac4b9b108190b440c991342df9b6 completed March 9, 2026, 11:17 a.m.
NED2 Entity disambiguation (via description) batch_69aeacc8445481908a3ae8bd62493413 completed March 9, 2026, 11:19 a.m.
Created at: March 1, 2026, 7:55 p.m.