Triple

T21259112
Position Surface form Disambiguated ID Type / Status
Subject Warendorf district E523949 entity
Predicate partOf P40 FINISHED
Object Münsterland 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: Münsterland | Statement: [Warendorf district, partOf, Münsterland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Münsterland
Context triple: [Warendorf district, partOf, Münsterland]
  • A. Münsterland chosen
    Münsterland is a rural region in northwestern Germany known for its historic castles, cycling routes, and traditional Westphalian culture.
  • B. Osnabrücker Land
    Osnabrücker Land is a region in Lower Saxony, Germany, centered around the city of Osnabrück and known for its historical towns, varied landscapes, and cultural heritage.
  • C. Weserbergland
    Weserbergland is a hilly, forested region in central Germany known for its picturesque landscapes along the Weser River and numerous historic towns.
  • D. Westfalen
    Westfalen is a historical region in northwestern Germany, now largely part of the state of North Rhine-Westphalia, known for its distinct cultural identity and medieval heritage.
  • E. South Westphalia
    South Westphalia is a region in western Germany known for its mixed industrial and rural character, encompassing parts of North Rhine-Westphalia including the Arnsberg area.
  • 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_69e0b5156d7881909bd4f83676590715 completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e735e53df88190bd6024793a0ada08 completed April 21, 2026, 8:31 a.m.
Created at: April 16, 2026, 3:59 p.m.