Triple

T22530292
Position Surface form Disambiguated ID Type / Status
Subject Roth district E557013 entity
Predicate contains P35 FINISHED
Object Rothsee 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: Rothsee | Statement: [Roth district, contains, Rothsee]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rothsee
Context triple: [Roth district, contains, Rothsee]
  • A. Rothsee chosen
    Rothsee is an artificial recreational lake in Middle Franconia, Bavaria, popular for swimming, sailing, and other water sports.
  • 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. Grimnitzsee
    Grimnitzsee is a natural lake in Brandenburg, Germany, known for its scenic forests, clear waters, and recreational opportunities near the town of Joachimsthal.
  • E. Fichtelsee
    Fichtelsee is a scenic artificial mountain lake in Germany’s Fichtelgebirge region, popular for hiking, swimming, and nature recreation.
  • 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_69e11e57483c8190b0887c4f8ff26446 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f15ed6734881908abbbee477dfab98 completed April 29, 2026, 1:28 a.m.
Created at: April 16, 2026, 8:51 p.m.