Triple

T18079045
Position Surface form Disambiguated ID Type / Status
Subject Autobahn A40 E432635 entity
Predicate passesThrough P225 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: [Autobahn A40, passesThrough, city of Dortmund]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: city of Dortmund
Context triple: [Autobahn A40, passesThrough, city of Dortmund]
  • A. Dortmund chosen
    Dortmund is a major city in western Germany known for its rich football culture, industrial heritage, and home club Borussia Dortmund.
  • B. 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.
  • C. Mönchengladbach
    Mönchengladbach is a city in western Germany known for its textile industry heritage and its football club Borussia Mönchengladbach.
  • D. Gelsenkirchen
    Gelsenkirchen is a city in western Germany known for its strong football culture and modern stadium, Veltins-Arena, home to FC Schalke 04.
  • E. Mülheim an der Ruhr
    Mülheim an der Ruhr is a city in western Germany’s Ruhr area, known for its industrial heritage, riverside setting on the Ruhr River, and role as a regional economic and cultural center.
  • 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_69d8b9070cac81909fa9473fb1c3f1c7 completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4d9f81ff08190b7465e3c5568aa59 completed April 19, 2026, 1:34 p.m.
Created at: April 10, 2026, 10:27 a.m.