Triple

T1924762
Position Surface form Disambiguated ID Type / Status
Subject Münster E40803 entity
Predicate partOf P40 FINISHED
Object Münster region E177404 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: Münster region | Statement: [Münster, partOf, Münster region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Münster region
Context triple: [Münster, partOf, Münster region]
  • A. Münsterland chosen
    Münsterland is a rural region in northwestern Germany known for its historic castles, cycling routes, and traditional Westphalian culture.
  • B. Oldenburg–Osnabrück region
    The Oldenburg–Osnabrück region is a metropolitan area in northwestern Germany centered around the cities of Oldenburg and Osnabrück, known for its mixed urban-rural character and strong regional economy.
  • C. Arnsberg region
    The Arnsberg region is an administrative district in the German state of North Rhine-Westphalia, encompassing several cities and towns in the eastern Ruhr and surrounding areas.
  • D. Osnabrück district
    Osnabrück district is a rural administrative district in the German state of Lower Saxony surrounding the independent city of Osnabrück.
  • E. Schwalm-Eder region
    The Schwalm-Eder region is a rural district in northern Hesse, Germany, known for its agricultural landscapes, small historic towns, and location south of Kassel.
  • 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_69a8864711648190b07bed24ed76258e completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb260da088190ac53bfc9437e112b completed March 7, 2026, 5:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69adf3e881748190b00f125185e91271 completed March 8, 2026, 10:10 p.m.
Created at: March 4, 2026, 7:35 p.m.