Triple

T15722057
Position Surface form Disambiguated ID Type / Status
Subject Suhl E381119 entity
Predicate locatedNear P294 FINISHED
Object Ilmenau E362158 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: Ilmenau | Statement: [Suhl, locatedNear, Ilmenau]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ilmenau
Context triple: [Suhl, locatedNear, Ilmenau]
  • A. Ilmenau chosen
    Ilmenau is a German town best known for its location in the Thuringian Forest and its association with the poet Johann Wolfgang von Goethe.
  • B. Eilenburg
    Eilenburg is a small historic town in the German state of Saxony, situated on the Mulde River northeast of Leipzig.
  • C. Sondershausen
    Sondershausen is a small town in the German state of Thuringia, known for its historic castle, former role as a princely residence, and long tradition of mining and music.
  • D. Altenburg
    Altenburg is a historic town in eastern Thuringia, Germany, known for its playing-card tradition and as the birthplace of the card game Skat.
  • E. Sangerhausen
    Sangerhausen is a town in the German state of Saxony-Anhalt, known for its historic mining heritage and its renowned Europa-Rosarium rose garden.
  • 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_69d86d9bf930819082b30cf6d169297c completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e04fb0b51081908e652ec4992296fa completed April 16, 2026, 2:55 a.m.
NED1 Entity disambiguation (via context triple) batch_6a002d966ffc8190aa0d9d3abf8ad593 completed May 10, 2026, 7:02 a.m.
Created at: April 10, 2026, 4:45 a.m.