Triple

T2136275
Position Surface form Disambiguated ID Type / Status
Subject Brandenburg E46660 entity
Predicate hasLake P1025 FINISHED
Object Müggelsee E157949 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: Müggelsee | Statement: [Brandenburg, hasLake, Müggelsee]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Müggelsee
Context triple: [Brandenburg, hasLake, Müggelsee]
  • A. Müggelsee chosen
    Müggelsee is the largest lake in Berlin, Germany, known for its popular recreational areas and natural surroundings.
  • B. Starnberger See
    Starnberger See is a large, scenic lake in southern Germany known for its affluent lakeside communities, recreational activities, and historical associations with Bavarian royalty.
  • C. 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.
  • D. Scharmützelsee
    Scharmützelsee is a popular lake in eastern Germany known for its scenic surroundings, recreational activities, and spa resorts.
  • E. Heiligensee
    Heiligensee is a residential and partly lakeside locality in the northwest of Berlin, known for its green spaces and village-like character within the borough of Reinickendorf.
  • 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_69a88a174ab48190a5db20c132e5dccf completed March 4, 2026, 7:37 p.m.
NER Named-entity recognition batch_69abbdc4ce8c81908d143d5451681e6a completed March 7, 2026, 5:55 a.m.
NED1 Entity disambiguation (via context triple) batch_69aeb3b13d9c8190b059d5c01518cf54 completed March 9, 2026, 11:49 a.m.
Created at: March 4, 2026, 7:44 p.m.