Triple

T12600312
Position Surface form Disambiguated ID Type / Status
Subject Bergisches Land E300838 entity
Predicate borders P224 FINISHED
Object Siegerland E742000 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: Siegerland | Statement: [Bergisches Land, borders, Siegerland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Siegerland
Context triple: [Bergisches Land, borders, Siegerland]
  • A. Siegerland chosen
    Siegerland is a hilly, forested region in western Germany known for its historic iron ore mining and metalworking industry.
  • B. Rhineland
    The Rhineland is a historically significant region in western Germany along the Rhine River, long contested as a strategic and economic heartland in European conflicts.
  • C. Westfalen
    Westfalen is a historical region in northwestern Germany, now largely part of the state of North Rhine-Westphalia, known for its distinct cultural identity and medieval heritage.
  • D. Rübeland
    Rübeland is a village in the Harz Mountains of central Germany, known for its show caves and scenic natural surroundings.
  • E. Münsterland
    Münsterland is a rural region in northwestern Germany known for its historic castles, cycling routes, and traditional Westphalian culture.
  • 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_69d7bdea2ca881908f379526c13b1145 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d954d1f6ac8190ab21ca7bcbc80129 completed April 10, 2026, 7:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69f65ec92c6c8190bd2d193e70940407 completed May 2, 2026, 8:30 p.m.
Created at: April 9, 2026, 5:09 p.m.