Triple

T10603333
Position Surface form Disambiguated ID Type / Status
Subject Trassenheide E275806 entity
Predicate locatedIn P40 FINISHED
Object Mecklenburg-Vorpommern E9737 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: Mecklenburg-Vorpommern | Statement: [Trassenheide, locatedIn, Mecklenburg-Vorpommern]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mecklenburg-Vorpommern
Context triple: [Trassenheide, locatedIn, Mecklenburg-Vorpommern]
  • A. Mecklenburg-Vorpommern chosen
    Mecklenburg-Vorpommern is a federal state in northeastern Germany known for its Baltic Sea coastline, numerous lakes, and relatively low population density.
  • B. Schleswig-Holstein
    Schleswig-Holstein is Germany’s northernmost state, known for its North Sea and Baltic Sea coastlines, maritime heritage, and shared border with Denmark.
  • C. Brandenburg
    Brandenburg is a federal state in northeastern Germany that surrounds Berlin and is known for its lakes, forests, and historic Prussian heritage.
  • D. Brandenburg
    Brandenburg is a small city in Meade County, Kentucky, situated along the Ohio River and serving as the county seat.
  • E. Thuringia
    Thuringia is a federal state in central Germany known for its forested landscapes, historic cities like Weimar and Erfurt, and its rich cultural and intellectual heritage.
  • 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_69dff76eef4c8190b4fe681a9431207d completed April 15, 2026, 8:39 p.m.
Created at: April 8, 2026, 7:32 p.m.