Triple

T11008652
Position Surface form Disambiguated ID Type / Status
Subject Kyffhäuser legend E260186 entity
Predicate hasSetting P3538 FINISHED
Object Kyffhäuser mountain E398897 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: Kyffhäuser mountain | Statement: [Kyffhäuser legend, hasSetting, Kyffhäuser mountain]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kyffhäuser mountain
Context triple: [Kyffhäuser legend, hasSetting, Kyffhäuser mountain]
  • A. Kyffhäuser hills chosen
    The Kyffhäuser hills are a low mountain range in central Germany known for the Kyffhäuser Monument and their association with the Barbarossa legend.
  • B. Vogelsberg
    Vogelsberg is a large volcanic mountain range in the German state of Hesse, known for its forested highlands and rural landscapes.
  • C. Schneeberg
    Schneeberg is a prominent alpine mountain in eastern Austria, known as the easternmost two-thousander of the Alps and a popular destination for hiking and skiing.
  • D. 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.
  • E. Hersbrucker Alb
    Hersbrucker Alb is a scenic low mountain and karst landscape in northern Bavaria, Germany, known for its rugged limestone formations, caves, and hiking trails.
  • 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_69d6aa9687448190b28d353b1b6a610e completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d7978810208190b8e2966ae67b6314 completed April 9, 2026, 12:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69e42d548b4481909dc73f834c704d44 completed April 19, 2026, 1:18 a.m.
Created at: April 8, 2026, 9:25 p.m.