Triple

T10848311
Position Surface form Disambiguated ID Type / Status
Subject Saxothuringian Zone E256073 entity
Predicate namedAfter P63 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: [Saxothuringian Zone, namedAfter, Saxony]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Saxony
Context triple: [Saxothuringian Zone, namedAfter, 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. Brandenburg
    Brandenburg is a small city in Meade County, Kentucky, situated along the Ohio River and serving as the county seat.
  • E. 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.
  • 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_69d6aa81a5d08190aa86689061d1ddd2 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d75114ca988190a0e730131adb2df0 completed April 9, 2026, 7:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69e344249f648190b541c7fad7a834f5 completed April 18, 2026, 8:43 a.m.
Created at: April 8, 2026, 9:20 p.m.