Triple

T5123756
Position Surface form Disambiguated ID Type / Status
Subject Fränkische Rezat E115534 entity
Predicate sourceRegion P410 FINISHED
Object Bavaria E7752 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: Bavaria | Statement: [Fränkische Rezat, sourceRegion, Bavaria]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bavaria
Context triple: [Fränkische Rezat, sourceRegion, Bavaria]
  • A. Bavaria chosen
    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.
  • B. 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.
  • 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. Pfalz
    Pfalz is a major wine-producing region in southwestern Germany known for its diverse vineyards and high-quality white wines.
  • E. Saarland
    Saarland is a small federal state in southwestern Germany known for its industrial history, Franco-German cultural influences, and location along the borders with France and Luxembourg.
  • 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_69bd4442ade0819087b9461f892b206b completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd7805c55c8190bc0540d755dc6242 completed March 20, 2026, 4:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69bec33b413081909cc7fcadd6997565 completed March 21, 2026, 4:11 p.m.
Created at: March 20, 2026, 1:42 p.m.