Triple

T7102899
Position Surface form Disambiguated ID Type / Status
Subject Wilseder Berg E165502 entity
Predicate locatedIn P40 FINISHED
Object Heidekreis E164519 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: Heidekreis | Statement: [Wilseder Berg, locatedIn, Heidekreis]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Heidekreis
Context triple: [Wilseder Berg, locatedIn, Heidekreis]
  • A. Heidekreis chosen
    Heidekreis is a rural district in Lower Saxony, Germany, known for its heath landscapes, nature reserves, and historic sites.
  • B. Hagen am Teutoburger Wald
    Hagen am Teutoburger Wald is a small municipality in Lower Saxony, Germany, situated near the Teutoburg Forest and close to the city of Osnabrück.
  • C. Kellerwald
    Kellerwald is a low mountain forest region in central Germany known for its ancient beech woodlands and protected national park status.
  • D. Gandersheim
    Gandersheim is a historic town in present-day Lower Saxony, Germany, best known for its medieval abbey and role as a cultural and religious center in the early Holy Roman Empire.
  • E. Benrath
    Benrath is a German surname most notably associated with the late actor Martin Benrath.
  • 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_69c6887fcddc8190a5d58908f6dee590 completed March 27, 2026, 1:39 p.m.
NER Named-entity recognition batch_69c6e58a0a2c819088e0c8874fb4491f completed March 27, 2026, 8:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7a3231f9c8190a19ddff3f5bf7cac completed March 28, 2026, 9:45 a.m.
Created at: March 27, 2026, 2:42 p.m.