Triple

T10078702
Position Surface form Disambiguated ID Type / Status
Subject Rheydt E213840 entity
Predicate integratedInto P77 FINISHED
Object city of Mönchengladbach E382016 NE FINISHED

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 Mönchengladbach | Statement: [Rheydt, integratedInto, city of Mönchengladbach]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: city of Mönchengladbach
Context triple: [Rheydt, integratedInto, city of Mönchengladbach]
  • A. Mönchengladbach chosen
    Mönchengladbach is a city in western Germany known for its textile industry heritage and its football club Borussia Mönchengladbach.
  • B. 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.
  • C. Dortmund
    Dortmund is a major city in western Germany known for its rich football culture, industrial heritage, and home club Borussia Dortmund.
  • 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 (3 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_69ca839bf730819086900c323c9b8c95 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cdd030a0fc819084b523e8e63636fa completed April 2, 2026, 2:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2b6550da881908fd5311e7b46ef24 completed April 5, 2026, 7:21 p.m.
Created at: March 30, 2026, 9 p.m.