Triple

T19598174
Position Surface form Disambiguated ID Type / Status
Subject Julius Kühn Institute E470400 entity
Predicate hasOfficeLocation P1268 FINISHED
Object Kleinmachnow NE NERFINISHED

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: Kleinmachnow | Statement: [Julius Kühn Institute, hasOfficeLocation, Kleinmachnow]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kleinmachnow
Context triple: [Julius Kühn Institute, hasOfficeLocation, Kleinmachnow]
  • A. Kleinmachnow chosen
    Kleinmachnow is a suburban municipality in the Potsdam-Mittelmark district of Brandenburg, Germany, located southwest of Berlin and known for its residential character and proximity to the capital.
  • B. Mariendorf
    Mariendorf is a locality in the Tempelhof-Schöneberg borough of Berlin, Germany, known for its residential character and the historic Mariendorf trotting racetrack.
  • C. Wandlitz
    Wandlitz is a municipality in the German state of Brandenburg, known for its lakes, forests, and proximity to Berlin.
  • D. Schwedt
    Schwedt is a town in northeastern Germany, located on the Oder River near the Polish border, known for its industrial facilities and cross-border regional ties.
  • E. Grömitz
    Grömitz is a Baltic Sea resort town in northern Germany known for its long sandy beaches and seaside tourism.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e510024481908415c0d616fa6186 completed April 10, 2026, 11:54 a.m.
NER Named-entity recognition batch_69e6407c52c081908704d3a4dd6e853b completed April 20, 2026, 3:04 p.m.
Created at: April 10, 2026, 1:43 p.m.