Triple

T13679440
Position Surface form Disambiguated ID Type / Status
Subject Dinslaken E327959 entity
Predicate hasNeighbouringMunicipality P224 FINISHED
Object Bottrop E220286 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: Bottrop | Statement: [Dinslaken, hasNeighbouringMunicipality, Bottrop]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bottrop
Context triple: [Dinslaken, hasNeighbouringMunicipality, Bottrop]
  • A. Bottrop chosen
    Bottrop is a city in western Germany’s Ruhr area, historically shaped by coal mining and industry.
  • B. Ruhrort
    Ruhrort is a historic inland port district of Duisburg in western Germany, known for its major role in Rhine and Ruhr river shipping and industry.
  • C. Remscheid
    Remscheid is a city in North Rhine-Westphalia, Germany, known historically for its metalworking industry and as the birthplace of physicist Wilhelm Röntgen.
  • D. Bergkamen
    Bergkamen is a town in North Rhine-Westphalia, Germany, known for its coal mining heritage and post-war planned urban development.
  • E. Hennef
    Hennef is a town in North Rhine-Westphalia, Germany, situated on the river Sieg near Bonn and known for its mix of residential areas, industry, and surrounding countryside.
  • 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_69d8076f1fa8819094664a59b55010df completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbc66cbb088190907cb89dda8e4ebd completed April 12, 2026, 4:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7944347a08190bc1386e78ddb3e71 completed May 3, 2026, 6:30 p.m.
Created at: April 9, 2026, 9:53 p.m.