Triple

T9525614
Position Surface form Disambiguated ID Type / Status
Subject Ruhrverband E229752 entity
Predicate basin P5506 FINISHED
Object Ruhr E80553 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: Ruhr | Statement: [Ruhrverband, basin, Ruhr]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ruhr
Context triple: [Ruhrverband, basin, Ruhr]
  • A. Ruhr chosen
    The Ruhr is a river in western Germany that flows through the Ruhr industrial region before joining the Rhine.
  • B. Emscher
    The Emscher is a river in Germany’s Ruhr industrial region, historically known for its heavy pollution and extensive canalization before major ecological restoration efforts.
  • C. North Rhine
    North Rhine is a historical region in western Germany that forms part of the larger Rhineland area along the Rhine River.
  • D. Rhein II
    Rhein II is a large-scale color photograph by German visual artist Andreas Gursky, renowned for its minimalist depiction of the Rhine River and for once being the most expensive photograph ever sold at auction.
  • E. Roer
    The Roer is a river in Western Europe that flows through parts of Belgium, Germany, and the Netherlands before joining the Meuse.
  • 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_69ca847870a881909d8d751a7d29da39 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd989b529c81909ee18dd3d468c816 completed April 1, 2026, 10:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1eab0860c819091b0169f82eac47f completed April 5, 2026, 4:53 a.m.
Created at: March 30, 2026, 7:59 p.m.