Triple

T23113526
Position Surface form Disambiguated ID Type / Status
Subject Schweriner See E576385 entity
Predicate hasPart P35 FINISHED
Object Äußerer See E1586001 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: Äußerer See | Statement: [Schweriner See, hasPart, Äußerer See]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Äußerer See
Context triple: [Schweriner See, hasPart, Äußerer See]
  • A. Neuer See
    Neuer See is a scenic lake in Berlin’s Tiergarten park, popular for boating and relaxation amid wooded surroundings.
  • B. Taching am See
    Taching am See is a small lakeside municipality in southeastern Bavaria, Germany, known for its scenic setting on Lake Taching and its rural, recreational character.
  • C. Stadtsee
    Stadtsee is a small lake located in the town of Bad Waldsee in southern Germany, known for its scenic setting and recreational use.
  • D. Innerer See chosen
    Innerer See is a smaller inner basin or sub-lake that forms part of the larger Schweriner See in northern Germany.
  • E. Kemnader See
    Kemnader See is an artificial lake and popular recreational area on the Ruhr River in western Germany, known for water sports, cycling paths, and leisure activities.
  • 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_69e245f4af548190898d434a64a1e774 completed April 17, 2026, 2:38 p.m.
NER Named-entity recognition batch_69f18e1121c08190a1d29fe594071c46 completed April 29, 2026, 4:50 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0c822f42cc81909ece73ff9a1f96df completed May 19, 2026, 3:30 p.m.
Created at: April 17, 2026, 3:59 p.m.