Triple

T7407560
Position Surface form Disambiguated ID Type / Status
Subject Dillenburg E170913 entity
Predicate locatedInRegion P40 FINISHED
Object Mittelhessen 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: Mittelhessen | Statement: [Dillenburg, locatedInRegion, Mittelhessen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mittelhessen
Context triple: [Dillenburg, locatedInRegion, Mittelhessen]
  • A. 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.
  • B. Greater Hesse
    Greater Hesse was a post–World War II administrative region in western Germany established by the U.S. occupation authorities, which later formed the core of the modern state of Hesse.
  • 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. Giessenlanden
    Giessenlanden was a former municipality in the Dutch province of South Holland that later became part of the newly formed municipality of Molenlanden.
  • E. 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.
  • 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_69c68a6010108190925e5284de022660 completed March 27, 2026, 1:47 p.m.
NER Named-entity recognition batch_69c6f298f2388190afc944c9bc78749a completed March 27, 2026, 9:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8111adbf48190a04df3cec1017b39 completed March 28, 2026, 5:34 p.m.
Created at: March 27, 2026, 3:10 p.m.