Triple

T4300659
Position Surface form Disambiguated ID Type / Status
Subject Kellerwald E99826 entity
Predicate near P350 FINISHED
Object Edersee E269254 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: Edersee | Statement: [Kellerwald, near, Edersee]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Edersee
Context triple: [Kellerwald, near, Edersee]
  • A. Edersee chosen
    Edersee is a large artificial reservoir in northern Hesse, Germany, created by the Eder Dam and known for recreation, water sports, and its scenic surroundings.
  • B. Möhnesee
    Möhnesee is a municipality in North Rhine-Westphalia, Germany, known for its large reservoir and scenic recreational area around the Möhne River.
  • C. Würmsee
    Würmsee is the historical name of the Bavarian lake now known as Starnberger See, one of Germany’s largest and most famous lakes near Munich.
  • D. Salzgittersee
    Salzgittersee is a large recreational lake in the city of Salzgitter, Germany, popular for swimming, water sports, and leisure activities.
  • E. Schlachtensee
    Schlachtensee is a lake and popular recreational area in southwestern Berlin, known for swimming, walking trails, and its surrounding forested landscape.
  • 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_69b345528ebc8190b5abc7e95094792d completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b3509fb2b88190a13ab88a5b924052 completed March 12, 2026, 11:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69b627cfba848190a56a25c6d7d750c7 completed March 15, 2026, 3:30 a.m.
Created at: March 12, 2026, 11:08 p.m.