Triple

T83536
Position Surface form Disambiguated ID Type / Status
Subject Chemnitz E1679 entity
Predicate isRegionalCenterFor P164 FINISHED
Object southwestern 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: southwestern Saxony | Statement: [Chemnitz, isRegionalCenterFor, southwestern Saxony]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: southwestern Saxony
Context triple: [Chemnitz, isRegionalCenterFor, southwestern 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. 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.
  • C. Franconia
    Franconia is a suburban community in Fairfax County, Northern Virginia, known for its residential neighborhoods and proximity to Washington, D.C.
  • D. Sachse
    Sachse is a suburban city in the Dallas–Fort Worth metropolitan area of northeastern Texas.
  • E. Lower Saxony
    Lower Saxony is a large federal state in northwestern Germany known for its diverse landscapes, strong industrial base, and historic cities such as Hanover and Göttingen.
  • 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_69a24c8150408190910a693eb51c1f71 completed Feb. 28, 2026, 2:01 a.m.
NER Named-entity recognition batch_69a2567c90308190a9b989c586f7e559 completed Feb. 28, 2026, 2:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69a29e45d6488190bc982137255c79f9 completed Feb. 28, 2026, 7:50 a.m.
Created at: Feb. 28, 2026, 2:06 a.m.