Triple

T7056993
Position Surface form Disambiguated ID Type / Status
Subject Glienicker Lake E164116 entity
Predicate hasNearbySettlement P4647 FINISHED
Object Berlin-Wannsee E4533 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: Berlin-Wannsee | Statement: [Glienicker Lake, hasNearbySettlement, Berlin-Wannsee]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Berlin-Wannsee
Context triple: [Glienicker Lake, hasNearbySettlement, Berlin-Wannsee]
  • A. Wannsee chosen
    Wannsee is a lakeside district in southwestern Berlin, Germany, known for its villa colonies, recreational waterfront, and as the site of the infamous 1942 Wannsee Conference.
  • B. Sachsenhausen
    Sachsenhausen is a district or neighborhood within the town of Giengen an der Brenz in the German state of Baden-Württemberg.
  • C. Sachsenhausen
    Sachsenhausen is a historic and culturally vibrant district of Frankfurt am Main, known for its traditional apple wine taverns, museums, and picturesque old town streets.
  • D. Spandau
    Spandau is a western borough of Berlin, Germany, known for its historic old town, fortress, and role as an important residential and industrial district.
  • E. Wannsee district
    Wannsee district is a lakeside area in southwestern Berlin known for its popular beaches, historic villas, and recreational waterfront attractions.
  • 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_69c68861678881909961ddf4d779f750 completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6e26a130c81908bdad15f5c4ae15d completed March 27, 2026, 8:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69c79c857ca08190a964861cb7aff86a completed March 28, 2026, 9:16 a.m.
Created at: March 27, 2026, 2:38 p.m.