Triple

T2123849
Position Surface form Disambiguated ID Type / Status
Subject Duisburg E43985 entity
Predicate hasDistrict P459 FINISHED
Object Duisburg-Süd
Duisburg-Süd is a southern borough of the German city of Duisburg, comprising several districts that are largely residential and industrial in character.
 E43985 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: Duisburg-Süd | Statement: [Duisburg, hasDistrict, Duisburg-Süd]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Duisburg-Süd
Context triple: [Duisburg, hasDistrict, Duisburg-Süd]
  • A. Duisburg
    Duisburg is a major industrial and port city in western Germany’s Ruhr region, known for its steel production and one of the world’s largest inland harbors.
  • B. Bottrop
    Bottrop is a city in western Germany’s Ruhr area, historically shaped by coal mining and industry.
  • C. Gelsenkirchen
    Gelsenkirchen is a city in western Germany known for its strong football culture and modern stadium, Veltins-Arena, home to FC Schalke 04.
  • D. Wuppertal
    Wuppertal is a city in western Germany known for its steep slopes, extensive parks, and the unique suspended monorail Wuppertal Schwebebahn.
  • E. Bergedorf
    Bergedorf is a historic quarter and former independent town in the southeast of Hamburg, Germany, known for its medieval castle and role as a regional administrative and trading 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: Duisburg-Süd
Triple: [Duisburg, hasDistrict, Duisburg-Süd]
Generated description
Duisburg-Süd is a southern borough of the German city of Duisburg, comprising several districts that are largely residential and industrial in character.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Duisburg-Süd
Target entity description: Duisburg-Süd is a southern borough of the German city of Duisburg, comprising several districts that are largely residential and industrial in character.
  • A. Duisburg chosen
    Duisburg is a major industrial and port city in western Germany’s Ruhr region, known for its steel production and one of the world’s largest inland harbors.
  • B. Bottrop
    Bottrop is a city in western Germany’s Ruhr area, historically shaped by coal mining and industry.
  • C. Gelsenkirchen
    Gelsenkirchen is a city in western Germany known for its strong football culture and modern stadium, Veltins-Arena, home to FC Schalke 04.
  • D. Wuppertal
    Wuppertal is a city in western Germany known for its steep slopes, extensive parks, and the unique suspended monorail Wuppertal Schwebebahn.
  • E. Bergedorf
    Bergedorf is a historic quarter and former independent town in the southeast of Hamburg, Germany, known for its medieval castle and role as a regional administrative and trading center.
  • F. None of above.

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_69a88717cfe48190b7ecdd68c824848a completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abbb55cb2c8190aab8199da3335032 completed March 7, 2026, 5:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae58cf2a588190a59dc1ae5d684538 completed March 9, 2026, 5:21 a.m.
NEDg Description generation batch_69ae599c6b288190b7e173ffc505c605 completed March 9, 2026, 5:24 a.m.
NED2 Entity disambiguation (via description) batch_69ae5a42675c8190a019034e8a6bda21 completed March 9, 2026, 5:27 a.m.
Created at: March 4, 2026, 7:44 p.m.