Triple

T12358857
Position Surface form Disambiguated ID Type / Status
Subject Province of Brandenburg E294679 entity
Predicate includedRegion P285 FINISHED
Object Havelland E242434 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: Havelland | Statement: [Province of Brandenburg, includedRegion, Havelland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Havelland
Context triple: [Province of Brandenburg, includedRegion, Havelland]
  • A. Havelland chosen
    Havelland is a rural district in western Brandenburg, Germany, known for its river landscapes along the Havel, historic towns, and agricultural character.
  • B. Emsland
    Emsland is a rural region in western Germany known for its agriculture, peatlands, and location along the River Ems near the Dutch border.
  • C. Havelte region
    The Havelte region is a rural area in the Dutch province of Drenthe known for its heathlands, prehistoric dolmens (hunebedden), and characteristic village landscapes.
  • D. Münsterland
    Münsterland is a rural region in northwestern Germany known for its historic castles, cycling routes, and traditional Westphalian culture.
  • E. Uckermark
    Uckermark is a rural historical region in northeastern Germany, known for its lakes, forests, and low population density, located primarily in the state of Brandenburg.
  • 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_69d6ab6d8a4081908636601e69ddf262 completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d93f90201481909359416b8b9f7871 completed April 10, 2026, 6:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69f63ef437d08190b09132fa090504a5 completed May 2, 2026, 6:14 p.m.
Created at: April 8, 2026, 9:54 p.m.