Triple

T10064539
Position Surface form Disambiguated ID Type / Status
Subject Idsteiner Senke E213066 entity
Predicate borderedBy P224 FINISHED
Object High Taunus E209318 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: High Taunus | Statement: [Idsteiner Senke, borderedBy, High Taunus]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: High Taunus
Context triple: [Idsteiner Senke, borderedBy, High Taunus]
  • A. Hochtaunus chosen
    Hochtaunus is a highland area in the central Taunus Mountains of Hesse, Germany, known for its forested hills, scenic landscapes, and popular hiking and recreation opportunities.
  • B. Kyffhäuser hills
    The Kyffhäuser hills are a low mountain range in central Germany known for the Kyffhäuser Monument and their association with the Barbarossa legend.
  • C. Hardtberg
    Hardtberg is a borough of the German city of Bonn, located in the western part of the city and comprising several residential and administrative districts.
  • D. Rhön
    Rhön is a low mountain range in central Germany known for its volcanic landscape, open plateaus, and designation as a UNESCO Biosphere Reserve.
  • E. Eifel Mountains
    The Eifel Mountains are a low mountain range in western Germany and eastern Belgium, known for their volcanic landscapes, dense forests, and picturesque villages.
  • 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_69ca83977128819084084eb7d1d8c52a completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cdcfd653748190aeddf7a679028604 completed April 2, 2026, 2:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2b630ca008190a337660ad8c9d57e completed April 5, 2026, 7:21 p.m.
Created at: March 30, 2026, 8:58 p.m.