Triple

T4198353
Position Surface form Disambiguated ID Type / Status
Subject Schlachtensee E86005 entity
Predicate nearby P350 FINISHED
Object 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: Wannsee | Statement: [Schlachtensee, nearby, Wannsee]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wannsee
Context triple: [Schlachtensee, nearby, 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. Neubukow
    Neubukow is a small town in northern Germany best known as the birthplace of archaeologist Heinrich Schliemann.
  • C. Zoppot
    Zoppot is the German name for the Baltic Sea resort city of Sopot, now located in northern Poland between Gdańsk and Gdynia.
  • D. Birkenwerder
    Birkenwerder is a small municipality in the German state of Brandenburg, located just north of Berlin and known for its residential character and surrounding forests.
  • E. Borsigwalde
    Borsigwalde is a residential locality in the Berlin borough of Reinickendorf, known for its industrial heritage linked to the Borsig engineering works.
  • 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_69aed93b89f48190a31f6d57c760e42f completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69af0360bc8081908ceb2483eef89174 completed March 9, 2026, 5:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69b58a12c11481908033229ecf90c9f9 completed March 14, 2026, 4:17 p.m.
Created at: March 9, 2026, 3:48 p.m.