Triple

T8883113
Position Surface form Disambiguated ID Type / Status
Subject Schwangau E211457 entity
Predicate hasBodyOfWater P1778 FINISHED
Object Alpsee E211458 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: Alpsee | Statement: [Schwangau, hasBodyOfWater, Alpsee]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Alpsee
Context triple: [Schwangau, hasBodyOfWater, Alpsee]
  • A. Alpsee chosen
    Alpsee is a picturesque alpine lake in Bavaria, Germany, renowned for its clear waters and scenic setting near the royal castles of Neuschwanstein and Hohenschwangau.
  • B. Hintersee
    Hintersee is a small Austrian municipality in the state of Salzburg, known for its scenic alpine landscapes and nearby mountain lake.
  • C. Hallwilersee
    Hallwilersee is a scenic lake in the Swiss cantons of Aargau and Lucerne, popular for recreation, boating, and nature conservation.
  • D. Alpnachersee
    Alpnachersee is a small alpine lake in central Switzerland that forms a narrow southwestern arm of Lake Lucerne near the town of Alpnach.
  • E. Tüttensee
    Tüttensee is a small lake in the Chiemgau region of Bavaria, Germany, known for its scenic setting and local recreational use.
  • 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_69ca838f9e20819096ab1f236a70381a completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc616b2d988190b923ef1e33aab787 completed April 1, 2026, 12:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69d29964080881909fd07de536667fbd completed April 5, 2026, 5:18 p.m.
Created at: March 30, 2026, 6:53 p.m.