Triple

T318146
Position Surface form Disambiguated ID Type / Status
Subject Bavaria E7752 entity
Predicate bordersGermanState P12928 FINISHED
Object Hesse E14304 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: Hesse | Statement: [Bavaria, bordersGermanState, Hesse]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hesse
Context triple: [Bavaria, bordersGermanState, Hesse]
  • A. Hesse chosen
    Hesse is a federal state in central Germany known for its financial hub Frankfurt am Main and its mix of urban centers, forests, and historic towns.
  • 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. Rhineland
    The Rhineland is a historically significant region in western Germany along the Rhine River, long contested as a strategic and economic heartland in European conflicts.
  • E. Harz
    Harz is a low mountain range in central Germany known for its dense forests, mining history, and association with German folklore such as the Brocken and Walpurgis Night.
  • 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_69a2e7e7af7881908890039d6be4e9b8 completed Feb. 28, 2026, 1:04 p.m.
NER Named-entity recognition batch_69a2ee016c408190beab4009653524db completed Feb. 28, 2026, 1:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69a4ab1724788190bd12ca956cd947ec completed March 1, 2026, 9:09 p.m.
Created at: Feb. 28, 2026, 1:08 p.m.