Triple

T17421932
Position Surface form Disambiguated ID Type / Status
Subject Kander Valley E423637 entity
Predicate hasAttraction P105 FINISHED
Object Blausee 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: Blausee | Statement: [Kander Valley, hasAttraction, Blausee]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Blausee
Context triple: [Kander Valley, hasAttraction, Blausee]
  • A. Blausee chosen
    Blausee is a small, crystal-clear alpine lake in the Swiss Bernese Oberland, famed for its striking blue waters and tranquil forest surroundings.
  • B. Obersee
    Obersee is a small, picturesque alpine lake in Bavaria, Germany, known for its clear emerald waters and dramatic mountain surroundings near the Königssee.
  • C. Ganderkesee
    Ganderkesee is a municipality in Lower Saxony, Germany, known for its rural character and proximity to the city of Bremen.
  • D. Schlei
    Schlei is a narrow Baltic Sea inlet in northern Germany that resembles a river and is known for its scenic landscapes and historic towns.
  • E. Oostvoornse Meer
    Oostvoornse Meer is a recreational lake in the Dutch province of South Holland, popular for activities such as diving, windsurfing, and nature walks.
  • 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_69d889d7d27c819088486ce3f0627fa1 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e44237f2cc819083ca0e7e00d828fb completed April 19, 2026, 2:47 a.m.
Created at: April 10, 2026, 5:46 a.m.