Triple

T2938246
Position Surface form Disambiguated ID Type / Status
Subject Oker E79320 entity
Predicate region P40 FINISHED
Object Harz E14581 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: Harz | Statement: [Oker, region, Harz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Harz
Context triple: [Oker, region, Harz]
  • A. Harz chosen
    Harz is a low mountain range in central Germany known for its dense forests, mining history, and association with German folklore such as the Brocken and Walpurgis Night.
  • B. Rhön
    Rhön is a low mountain range in central Germany known for its volcanic landscape, open plateaus, and designation as a UNESCO Biosphere Reserve.
  • C. Thuringian Forest
    The Thuringian Forest is a low mountain range in central Germany known for its dense woodlands, scenic hiking trails, and the historic Rennsteig ridgeway.
  • D. Herzberg am Harz
    Herzberg am Harz is a small town in Lower Saxony, Germany, located on the southern edge of the Harz Mountains and known for its historic castle and timber-framed architecture.
  • E. Franconian Forest
    The Franconian Forest is a low mountain range in northern Bavaria, Germany, known for its extensive woodlands, hiking trails, and role as a watershed between the Main and Saale river systems.
  • 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_69ad8b0fbab081908f6a61567c045d8d completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad986c1c0c8190a6a9f17082438cfd completed March 8, 2026, 3:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69b224a78bbc8190b4f4cdb058d5a176 completed March 12, 2026, 2:27 a.m.
Created at: March 8, 2026, 2:56 p.m.