Triple

T13821338
Position Surface form Disambiguated ID Type / Status
Subject Five Lakes Region E332140 entity
Predicate hasPart P35 FINISHED
Object Pilsensee E903506 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: Pilsensee | Statement: [Five Lakes Region, hasPart, Pilsensee]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pilsensee
Context triple: [Five Lakes Region, hasPart, Pilsensee]
  • A. Pilsensee chosen
    Pilsensee is a small scenic lake in Bavaria, Germany, known for its clear waters, recreational opportunities, and location within the popular Five Lakes Region near Munich.
  • B. Senftenberger See
    Senftenberger See is an artificial lake in Brandenburg, Germany, created from a former open-cast lignite mine and now used as a popular recreational and water sports area.
  • C. Grunewaldsee
    Grunewaldsee is a popular forest lake in Berlin known for its scenic surroundings and dog-friendly bathing areas.
  • D. Rothsee
    Rothsee is an artificial recreational lake in Middle Franconia, Bavaria, popular for swimming, sailing, and other water sports.
  • E. Weissensee
    Weissensee is a picturesque alpine lake and surrounding region in southern Austria, renowned for its clear waters, outdoor recreation, and unspoiled natural 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_69d81c59f8808190a851bc56afdc55e9 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de0284428081908043c55caeefb833 completed April 14, 2026, 9:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69fcb648146c8190842a3da4e4c0e217 completed May 7, 2026, 3:56 p.m.
Created at: April 9, 2026, 10:12 p.m.