Triple

T4407435
Position Surface form Disambiguated ID Type / Status
Subject Berlin lake system E93768 entity
Predicate hasPart P35 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: [Berlin lake system, hasPart, Müggelsee]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Müggelsee
Context triple: [Berlin lake system, hasPart, 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. Griebnitzsee
    Griebnitzsee is a lake on the southwestern outskirts of Berlin, Germany, known for its scenic waterfront, historic villas, and role as part of the former inner German border.
  • D. 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.
  • E. Scharmützelsee
    Scharmützelsee is a popular lake in eastern Germany known for its scenic surroundings, recreational activities, and spa resorts.
  • 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_69b345158c748190a2c040fce2da9980 completed March 12, 2026, 10:58 p.m.
NER Named-entity recognition batch_69b3548b1ca08190b3136867c7098d86 completed March 13, 2026, 12:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69b6374a22c88190ba36ddb180634573 completed March 15, 2026, 4:36 a.m.
Created at: March 12, 2026, 11:28 p.m.