Triple

T509978
Position Surface form Disambiguated ID Type / Status
Subject Prussia E10584 entity
Predicate territoryIncludes P285 FINISHED
Object Brandenburg E46660 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: Brandenburg | Statement: [Prussia, territoryIncludes, Brandenburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Brandenburg
Context triple: [Prussia, territoryIncludes, Brandenburg]
  • A. Brandenburg chosen
    Brandenburg is a federal state in northeastern Germany that surrounds Berlin and is known for its lakes, forests, and historic Prussian heritage.
  • B. Mecklenburg-Vorpommern
    Mecklenburg-Vorpommern is a federal state in northeastern Germany known for its Baltic Sea coastline, numerous lakes, and relatively low population density.
  • C. Saxony
    Saxony is a historic region and former kingdom in eastern Germany, known for its cultural centers like Dresden and Leipzig and its significant role in Central European history.
  • D. 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.
  • E. Saxony-Anhalt
    Saxony-Anhalt is a federal state in central Germany known for its rich cultural heritage, including numerous UNESCO World Heritage Sites such as the Bauhaus in Dessau and the historic towns of Quedlinburg and Wittenberg.
  • 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_69a2e848adf881908e5e04f7af030093 completed Feb. 28, 2026, 1:06 p.m.
NER Named-entity recognition batch_69a2f164a9d48190b525a97b5c06ffe2 completed Feb. 28, 2026, 1:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69a7927a25b081909b553fe3a486e84a completed March 4, 2026, 2:01 a.m.
Created at: Feb. 28, 2026, 1:12 p.m.