Triple

T3652492
Position Surface form Disambiguated ID Type / Status
Subject Hase E77450 entity
Predicate region P40 FINISHED
Object Emsland district E47544 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: Emsland district | Statement: [Hase, region, Emsland district]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Emsland district
Context triple: [Hase, region, Emsland district]
  • A. Emsland chosen
    Emsland is a rural region in western Germany known for its agriculture, peatlands, and location along the River Ems near the Dutch border.
  • B. Northeim district
    Northeim district is a rural administrative district in southern Lower Saxony, Germany, known for its agricultural landscape, small towns, and location along the River Leine.
  • C. Osnabrück district
    Osnabrück district is a rural administrative district in the German state of Lower Saxony surrounding the independent city of Osnabrück.
  • D. Detmold region
    The Detmold region is an administrative region in the German state of North Rhine-Westphalia, encompassing several districts and towns in the eastern part of the state.
  • E. Hildesheim district
    Hildesheim district is a rural administrative district in Lower Saxony, Germany, surrounding the city of Hildesheim and comprising several towns and municipalities.
  • 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_69ad85def5cc8190863dccf55a18bebb completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adc3b664448190986b5223d59de282 completed March 8, 2026, 6:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4c38f989c8190befc64db51041a53 completed March 14, 2026, 2:10 a.m.
Created at: March 8, 2026, 3:24 p.m.