Triple

T3874825
Position Surface form Disambiguated ID Type / Status
Subject Arnsberg region E92473 entity
Predicate borderedBy P224 FINISHED
Object Giessen region E109575 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: Giessen region | Statement: [Arnsberg region, borderedBy, Giessen region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Giessen region
Context triple: [Arnsberg region, borderedBy, Giessen region]
  • A. Schweinfurt region
    The Schweinfurt region is an area in northern Bavaria, Germany, centered around the city of Schweinfurt and known for its Franconian cultural and historical heritage.
  • B. Ansbach region
    The Ansbach region is an area in the German state of Bavaria, historically part of Franconia and known for its distinct East Franconian dialect and cultural heritage.
  • C. South Hesse
    South Hesse is a region in the southern part of the German state of Hesse that includes major urban and economic centers such as Darmstadt and the Rhine-Main area.
  • D. Northern Hesse region
    The Northern Hesse region is a historical area in central Germany that once formed part of the territorial domain of the Prince of Waldeck.
  • E. Middle Hesse chosen
    Middle Hesse is a central region of the German state of Hesse known for its mix of historic university towns, industrial centers, and rural landscapes.
  • 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_69aed967448c819086c4b358d37b25aa completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeec59bea08190b1e193f34944a2ee completed March 9, 2026, 3:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69b51c87214881908e03f5c770c58713 completed March 14, 2026, 8:29 a.m.
Created at: March 9, 2026, 3:20 p.m.