Triple

T7966017
Position Surface form Disambiguated ID Type / Status
Subject Bad Lausick E185206 entity
Predicate countrySubdivision P766 FINISHED
Object Saxony E11465 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: Saxony | Statement: [Bad Lausick, countrySubdivision, Saxony]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Saxony
Context triple: [Bad Lausick, countrySubdivision, Saxony]
  • A. Saxony chosen
    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.
  • B. 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.
  • 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. Bavaria
    Bavaria is a historic region and federal state in southeastern Germany, known for its distinct cultural traditions, large size and population, and major cities such as Munich.
  • 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_69ca8297699481909b75a405f01e03af completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3ba262208190887169fe94e47b0e completed March 31, 2026, 3:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69ccbd8698a88190b5f9b4d232504f04 completed April 1, 2026, 6:39 a.m.
Created at: March 30, 2026, 5:12 p.m.