Triple

T10603356
Position Surface form Disambiguated ID Type / Status
Subject Trassenheide E275806 entity
Predicate locatedNear P294 FINISHED
Object Karlshagen E291724 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: Karlshagen | Statement: [Trassenheide, locatedNear, Karlshagen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Karlshagen
Context triple: [Trassenheide, locatedNear, Karlshagen]
  • A. Karlshagen chosen
    Karlshagen is a seaside resort village on the Baltic coast of northeastern Germany, located on the island of Usedom and known for its sandy beaches and tourism.
  • B. Lankwitz
    Lankwitz is a residential locality in the southwestern part of Berlin, known for its quiet neighborhoods, green spaces, and mix of historic and modern architecture.
  • C. Katharinenfeld
    Katharinenfeld was the historical German settler colony that later became the town of Bolnisi in southern Georgia.
  • D. Karsdorf
    Karsdorf is a small municipality in the German state of Saxony-Anhalt, known for its location along the Unstrut River and its surrounding wine-growing and agricultural landscape.
  • E. Riedenburg
    Riedenburg is a small Bavarian town in southern Germany known for its scenic location in the Altmühl Valley and its historic castles.
  • 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_69d6aaf948d88190806cc3a8c47a3fb2 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d6ded6d698819084f96f46ea941461 completed April 8, 2026, 11:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69e343cd76448190b0583cc15005ac9d completed April 18, 2026, 8:41 a.m.
Created at: April 8, 2026, 7:32 p.m.