Triple

T3737573
Position Surface form Disambiguated ID Type / Status
Subject Trauen E79621 entity
Predicate locatedIn P40 FINISHED
Object Lüneburg Heath region E29351 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: Lüneburg Heath region | Statement: [Trauen, locatedIn, Lüneburg Heath region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lüneburg Heath region
Context triple: [Trauen, locatedIn, Lüneburg Heath region]
  • A. Lüneburg Heath chosen
    Lüneburg Heath is a large heath and nature reserve in northern Germany known for its purple heather landscapes, historic villages, and protected wildlife habitats.
  • 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. Sauerland
    Sauerland is a hilly, forested region in western Germany known for its reservoirs, outdoor recreation, and winter sports areas.
  • D. Münsterland
    Münsterland is a rural region in northwestern Germany known for its historic castles, cycling routes, and traditional Westphalian culture.
  • E. Mühlenbecker Land
    Mühlenbecker Land is a municipality in the Oberhavel district of Brandenburg, Germany, known for its proximity to Berlin and its mix of forests, lakes, and residential areas.
  • 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_69ad8b115610819095b02007da5ca3cb completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adcb3e9248819098d481fe29e1c628 completed March 8, 2026, 7:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4db20bdfc81909cd27278ff5d9716 completed March 14, 2026, 3:50 a.m.
Created at: March 8, 2026, 3:34 p.m.