Triple

T6677377
Position Surface form Disambiguated ID Type / Status
Subject Oberhof E151886 entity
Predicate nearbyCity P350 FINISHED
Object Suhl E381119 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: Suhl | Statement: [Oberhof, nearbyCity, Suhl]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Suhl
Context triple: [Oberhof, nearbyCity, Suhl]
  • A. Suhl chosen
    Suhl is a city in central Germany known historically as a center of firearms manufacturing and located in the federal state of Thuringia.
  • B. Melsungen
    Melsungen is a small historic town in northern Hesse, Germany, known for its well-preserved half-timbered houses and picturesque setting on the Fulda River.
  • C. Borgholzhausen
    Borgholzhausen is a small town in North Rhine-Westphalia, Germany, known for its location on the Teutoburg Forest and its historical ties to the former County of Ravensberg.
  • D. Gevelsberg
    Gevelsberg is a town in North Rhine-Westphalia, Germany, situated in the Ennepe-Ruhr district within the Ruhr metropolitan region.
  • E. Fritzlar
    Fritzlar is a historic town in northern Hesse, Germany, known for its well-preserved medieval old town and its significance in early German Christian history.
  • 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_69c687f830bc81909eb8b04dbb8450b1 completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6b0f53af48190b0b25b61c3531158 completed March 27, 2026, 4:31 p.m.
NED1 Entity disambiguation (via context triple) batch_69c972177054819083dc93ad7f109b49 completed March 29, 2026, 6:40 p.m.
Created at: March 27, 2026, 2:03 p.m.