Triple

T2982971
Position Surface form Disambiguated ID Type / Status
Subject Ruhr E80553 entity
Predicate sourceLocation P40 FINISHED
Object Sauerland E232082 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: Sauerland | Statement: [Ruhr, sourceLocation, Sauerland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sauerland
Context triple: [Ruhr, sourceLocation, Sauerland]
  • A. Sauerland chosen
    Sauerland is a hilly, forested region in western Germany known for its reservoirs, outdoor recreation, and winter sports areas.
  • B. Weser Uplands
    The Weser Uplands is a hilly, forested region in central Germany known for its picturesque landscapes, traditional half-timbered towns, and association with many of the Brothers Grimm fairy tales.
  • C. Lüneburg Heath
    Lüneburg Heath is a large heath and nature reserve in northern Germany known for its purple heather landscapes, historic villages, and protected wildlife habitats.
  • D. Odenwald
    Odenwald is a low mountain range in southwestern Germany known for its forested hills, historic towns, and scenic hiking landscapes.
  • E. Harz
    Harz is a low mountain range in central Germany known for its dense forests, mining history, and association with German folklore such as the Brocken and Walpurgis Night.
  • 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_69ad8b15f6ac8190be5fd16a33edcb4f completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad99a1ed44819085ae6d39943db1d9 completed March 8, 2026, 3:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69b1f8629c7c8190b597255ab8f391af completed March 11, 2026, 11:18 p.m.
Created at: March 8, 2026, 2:58 p.m.