Triple

T2238091
Position Surface form Disambiguated ID Type / Status
Subject Suebi E49327 entity
Predicate influencedToponym P20713 FINISHED
Object Swabia E80742 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: Swabia | Statement: [Suebi, influencedToponym, Swabia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Swabia
Context triple: [Suebi, influencedToponym, Swabia]
  • A. Swabia (Bavaria)
    Swabia (Bavaria) is an administrative region in southwestern Bavaria, Germany, known for its distinct Swabian cultural heritage and mix of industrial cities and rural landscapes.
  • B. Franconia
    Franconia is a historical region in northern Bavaria, Germany, known for its medieval towns, rich cultural heritage, and distinct Franconian identity within the German-speaking world.
  • C. Franconia
    Franconia is a suburban community in Fairfax County, Northern Virginia, known for its residential neighborhoods and proximity to Washington, D.C.
  • D. Duchy of Swabia chosen
    The Duchy of Swabia was a major medieval stem duchy in southwestern Germany that played a key role in the politics and dynastic struggles of the Holy Roman Empire.
  • 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_69a88aa84bdc819086df50e9c20b301e completed March 4, 2026, 7:40 p.m.
NER Named-entity recognition batch_69abc5b262488190b6455d1d28d2306d completed March 7, 2026, 6:29 a.m.
NED1 Entity disambiguation (via context triple) batch_69b12de0989481909ce71f3fb739ac2a completed March 11, 2026, 8:54 a.m.
Created at: March 4, 2026, 7:47 p.m.