Triple

T15968867
Position Surface form Disambiguated ID Type / Status
Subject Hiddenhausen E387267 entity
Predicate hasSubdivision P747 FINISHED
Object Sundern
Sundern is a locality that forms one of the subdivisions of the municipality of Hiddenhausen in North Rhine-Westphalia, Germany.
E1186066 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: Sundern | Statement: [Hiddenhausen, hasSubdivision, Sundern]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sundern
Context triple: [Hiddenhausen, hasSubdivision, Sundern]
  • A. Sundern
    Sundern is a town in the Hochsauerland district of North Rhine-Westphalia, Germany, known for its proximity to the Sorpe Dam and the surrounding Sauerland recreational region.
  • B. Süddorf
    Süddorf is a small village on the North Sea island of Amrum in northern Germany, known for its traditional Frisian character and coastal surroundings.
  • C. Winsum
    Winsum is a historic village and former municipality in the Dutch province of Groningen, known for its old churches, windmills, and picturesque canals.
  • D. Nottuln
    Nottuln is a historic municipality in North Rhine-Westphalia, Germany, known for its medieval architecture and role in regional conflicts.
  • E. Teesdorf
    Teesdorf is a municipality in Lower Austria known for its motorsport testing facilities and rural setting south of Vienna.
  • 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: Sundern
Triple: [Hiddenhausen, hasSubdivision, Sundern]
Generated description
Sundern is a locality that forms one of the subdivisions of the municipality of Hiddenhausen in North Rhine-Westphalia, Germany.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Sundern
Target entity description: Sundern is a locality that forms one of the subdivisions of the municipality of Hiddenhausen in North Rhine-Westphalia, Germany.
  • A. Sundern
    Sundern is a town in the Hochsauerland district of North Rhine-Westphalia, Germany, known for its proximity to the Sorpe Dam and the surrounding Sauerland recreational region.
  • B. Süddorf
    Süddorf is a small village on the North Sea island of Amrum in northern Germany, known for its traditional Frisian character and coastal surroundings.
  • C. Winsum
    Winsum is a historic village and former municipality in the Dutch province of Groningen, known for its old churches, windmills, and picturesque canals.
  • D. Nottuln
    Nottuln is a historic municipality in North Rhine-Westphalia, Germany, known for its medieval architecture and role in regional conflicts.
  • E. Teesdorf
    Teesdorf is a municipality in Lower Austria known for its motorsport testing facilities and rural setting south of Vienna.
  • 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_69d86da94ccc819083d187f5dc6a123e completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e1572847f08190830e30125e829766 completed April 16, 2026, 9:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffbe88fa308190942d37cf67458396 completed May 9, 2026, 11:08 p.m.
NEDg Description generation batch_69ffbf3e80b08190899262a9d03c0e93 completed May 9, 2026, 11:11 p.m.
NED2 Entity disambiguation (via description) batch_69ffbfc0d1548190b7d2e9e10e837f0b completed May 9, 2026, 11:14 p.m.
Created at: April 10, 2026, 4:54 a.m.