Triple

T5357126
Position Surface form Disambiguated ID Type / Status
Subject Dessau E102721 entity
Predicate twinnedWith P1072 FINISHED
Object Ibbenbüren
Ibbenbüren is a town in North Rhine-Westphalia, Germany, known historically for its coal mining and situated near the Teutoburg Forest.
E569686 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: Ibbenbüren | Statement: [Dessau, twinnedWith, Ibbenbüren]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ibbenbüren
Context triple: [Dessau, twinnedWith, Ibbenbüren]
  • A. Bentheim
    Bentheim is a historical county in Lower Saxony, Germany, known for its Reformed Protestant heritage and the former County of Bentheim.
  • B. Radevormwald
    Radevormwald is a small historic town in North Rhine-Westphalia, western Germany, known for its hilly Bergisches Land landscape and traditional textile and metalworking industries.
  • C. Lüdenscheid
    Lüdenscheid is a town in western Germany’s Sauerland region, historically noted for its role in World War II and known today for its metal and plastics industries.
  • D. Bergkamen
    Bergkamen is a town in North Rhine-Westphalia, Germany, known for its coal mining heritage and post-war planned urban development.
  • E. Nienburg
    Nienburg is a historic town in Lower Saxony, Germany, known for its medieval architecture and scenic location along the Weser River.
  • 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: Ibbenbüren
Triple: [Dessau, twinnedWith, Ibbenbüren]
Generated description
Ibbenbüren is a town in North Rhine-Westphalia, Germany, known historically for its coal mining and situated near the Teutoburg Forest.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Ibbenbüren
Target entity description: Ibbenbüren is a town in North Rhine-Westphalia, Germany, known historically for its coal mining and situated near the Teutoburg Forest.
  • A. Bentheim
    Bentheim is a historical county in Lower Saxony, Germany, known for its Reformed Protestant heritage and the former County of Bentheim.
  • B. Radevormwald
    Radevormwald is a small historic town in North Rhine-Westphalia, western Germany, known for its hilly Bergisches Land landscape and traditional textile and metalworking industries.
  • C. Lüdenscheid
    Lüdenscheid is a town in western Germany’s Sauerland region, historically noted for its role in World War II and known today for its metal and plastics industries.
  • D. Bergkamen
    Bergkamen is a town in North Rhine-Westphalia, Germany, known for its coal mining heritage and post-war planned urban development.
  • E. Nienburg
    Nienburg is a historic town in Lower Saxony, Germany, known for its medieval architecture and scenic location along the Weser River.
  • 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_69bd43d8f7248190b64c140734b5c9a8 completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd863099b081909d20f7014b98de5a completed March 20, 2026, 5:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69c124dc89a88190a219eda67e6d933c completed March 23, 2026, 11:32 a.m.
NEDg Description generation batch_69c129f2d84481908808481ba48a5aed completed March 23, 2026, 11:54 a.m.
NED2 Entity disambiguation (via description) batch_69c12a5235288190a8db827e1a610cd0 completed March 23, 2026, 11:56 a.m.
Created at: March 20, 2026, 2:01 p.m.