Triple

T8371029
Position Surface form Disambiguated ID Type / Status
Subject Heinrich von Kleist E197453 entity
Predicate placeOfDeath P21 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: [Heinrich von Kleist, placeOfDeath, Wannsee]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wannsee
Context triple: [Heinrich von Kleist, placeOfDeath, 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. Kaufering
    Kaufering is a municipality in Bavaria, Germany, known historically for its World War II subcamps of Dachau and its location near the town of Landsberg am Lech.
  • C. Pulhof
    Pulhof is a residential neighborhood in the Antwerp district of Berchem, Belgium, known for its quiet streets and urban character.
  • D. Neubukow
    Neubukow is a small town in northern Germany best known as the birthplace of archaeologist Heinrich Schliemann.
  • E. Zinnowitz
    Zinnowitz is a seaside resort town on Germany’s Baltic Sea coast, known for its sandy beaches, historic spa architecture, and tourism on the island of Usedom.
  • 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_69ca82f56730819080cec5d991c76f4c completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb80a509dc81909e0ea4c66b21d84f completed March 31, 2026, 8:07 a.m.
NED1 Entity disambiguation (via context triple) batch_69cdc79752b48190933ea644a59f2305 completed April 2, 2026, 1:34 a.m.
Created at: March 30, 2026, 6:01 p.m.