Triple

T10171641
Position Surface form Disambiguated ID Type / Status
Subject Sorpedamm E235343 entity
Predicate hasNameInLanguage P15 FINISHED
Object Sorpedamm@de E235343 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: Sorpedamm@de | Statement: [Sorpedamm, hasNameInLanguage, Sorpedamm@de]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sorpedamm@de
Context triple: [Sorpedamm, hasNameInLanguage, Sorpedamm@de]
  • A. Sorpedamm chosen
    Sorpedamm is a reservoir dam in North Rhine-Westphalia, Germany, primarily used for water supply, flood control, and recreation.
  • B. Wilmersdorf
    Wilmersdorf is a residential district in southwestern Berlin known for its affluent neighborhoods, shopping streets like Kurfürstendamm, and a mix of historic and modern architecture.
  • C. Schorfheide
    Schorfheide is a large forested and lake-rich area in Brandenburg, Germany, known for its protected natural landscapes and historical use as a royal and political hunting ground.
  • D. Friedrichswerder, Berlin
    Friedrichswerder is a historic inner-city quarter of Berlin known for its 19th-century architecture and cultural landmarks near the city’s political and museum districts.
  • E. Berlin-Rahnsdorf
    Berlin-Rahnsdorf is a lakeside locality in the eastern part of Berlin, Germany, known for its natural scenery, forests, and waterfront recreation areas.
  • 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_69ca84ceafd0819085828600e11bed6b completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cdec9e4e0c819097dceb7bf7757948 completed April 2, 2026, 4:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69d30101e3ec819095a587c0dae55f71 completed April 6, 2026, 12:40 a.m.
Created at: March 30, 2026, 9:10 p.m.