Triple

T20345205
Position Surface form Disambiguated ID Type / Status
Subject Dortmund U-Tower E495847 entity
Predicate operator P179 FINISHED
Object City of Dortmund NE NERFINISHED

How this triple was built (2 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: City of Dortmund | Statement: [Dortmund U-Tower, operator, City of Dortmund]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: City of Dortmund
Context triple: [Dortmund U-Tower, operator, City of Dortmund]
  • A. Imperial City of Dortmund
    The Imperial City of Dortmund was a significant free imperial city within the Holy Roman Empire, known as a regional commercial and political center in what is now western Germany.
  • B. Dortmund chosen
    Dortmund is a major city in western Germany known for its rich football culture, industrial heritage, and home club Borussia Dortmund.
  • C. Mönchengladbach
    Mönchengladbach is a city in western Germany known for its textile industry heritage and its football club Borussia Mönchengladbach.
  • D. City of Essen
    The City of Essen is a major urban center in Germany’s Ruhr area, historically significant as a medieval ecclesiastical seat and later as an important industrial and coal-mining hub.
  • E. Bergisch Gladbach
    Bergisch Gladbach is a city in North Rhine-Westphalia, western Germany, known for its paper industry, proximity to Cologne, and surrounding Bergisches Land countryside.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4a3320881909495ae8bc30bc2dc completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e67838744481909069b76b25dd4bb9 completed April 20, 2026, 7:02 p.m.
Created at: April 16, 2026, 11:24 a.m.