Triple

T2025197
Position Surface form Disambiguated ID Type / Status
Subject Upper Bavaria E44191 entity
Predicate contains P35 FINISHED
Object Starnberger See E41860 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: Starnberger See | Statement: [Upper Bavaria, contains, Starnberger See]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Starnberger See
Context triple: [Upper Bavaria, contains, Starnberger See]
  • A. Starnberger See chosen
    Starnberger See is a large, scenic lake in southern Germany known for its affluent lakeside communities, recreational activities, and historical associations with Bavarian royalty.
  • B. Jungfernsee
    Jungfernsee is a scenic lake on the outskirts of Potsdam and Berlin, known for its historic villas, palaces, and location along the former inner German border.
  • C. Ammersee
    Ammersee is a large glacial lake in southern Germany known for its scenic shores, recreational activities, and proximity to the Alps.
  • D. Müggelsee
    Müggelsee is the largest lake in Berlin, Germany, known for its popular recreational areas and natural surroundings.
  • E. Eibsee
    Eibsee is a picturesque alpine lake in Bavaria, Germany, renowned for its clear turquoise waters and dramatic setting at the foot of the Zugspitze.
  • 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_69a8891201bc8190aca837be6de41579 completed March 4, 2026, 7:33 p.m.
NER Named-entity recognition batch_69abb8f3faa08190a48ae1355d6e009f completed March 7, 2026, 5:34 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae517e7c2481909d2840a734eed96b completed March 9, 2026, 4:50 a.m.
Created at: March 4, 2026, 7:38 p.m.