Triple

T19968253
Position Surface form Disambiguated ID Type / Status
Subject Eckersmühlen E479999 entity
Predicate locatedIn P40 FINISHED
Object Bavaria, Germany NE NERFINISHED

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, Germany | Statement: [Eckersmühlen, locatedIn, Bavaria, Germany]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bavaria, Germany
Context triple: [Eckersmühlen, locatedIn, Bavaria, Germany]
  • 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. Hesse, Germany
    Hesse, Germany is a federal state in central Germany known for its financial hub Frankfurt am Main, forested landscapes, and historic cities such as Wiesbaden and Kassel.
  • C. Fürth, Bavaria, Germany
    Fürth is a historic city in the German state of Bavaria, now part of the Nuremberg metropolitan area and known for its rich cultural heritage and Jewish history.
  • D. Baviera
    Baviera is a barangay, or local administrative village, within the city of Sagay in the Philippines.
  • E. Baden, Germany
    Baden, Germany is a historical region in southwestern Germany, formerly a grand duchy, known for its spa towns, wine-growing areas, and location along the Rhine.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e523c19881909f9197037200dde6 completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e65bc6b0208190b1ae30be95712326 completed April 20, 2026, 5 p.m.
Created at: April 10, 2026, 1:54 p.m.