Triple

T129339
Position Surface form Disambiguated ID Type / Status
Subject Fürth E2619 entity
Predicate locatedIn P40 FINISHED
Object Franconia 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: Franconia | Statement: [Fürth, locatedIn, Franconia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Franconia
Context triple: [Fürth, locatedIn, Franconia]
  • A. Franconia
    Franconia is a suburban community in Fairfax County, Northern Virginia, known for its residential neighborhoods and proximity to Washington, D.C.
  • B. 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.
  • C. Hesse
    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.
  • D. Westphalia
    Westphalia is a historical region in northwestern Germany known for being the site of the 1648 treaties that ended the Thirty Years' War and reshaped the political order of Europe.
  • 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_69a2520c0f3481908b0ed054a2fca8d0 completed Feb. 28, 2026, 2:25 a.m.
NER Named-entity recognition batch_69a2576518e0819096b35d8af7a4d1bd completed Feb. 28, 2026, 2:48 a.m.
NED1 Entity disambiguation (via context triple) batch_69a3a5cd067c8190a25649f80c69ee89 completed March 1, 2026, 2:34 a.m.
Created at: Feb. 28, 2026, 2:30 a.m.