Triple

T5198100
Position Surface form Disambiguated ID Type / Status
Subject Salzburg-Umgebung District E117321 entity
Predicate hasMunicipality P847 FINISHED
Object Eugendorf E486501 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: Eugendorf | Statement: [Salzburg-Umgebung District, hasMunicipality, Eugendorf]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Eugendorf
Context triple: [Salzburg-Umgebung District, hasMunicipality, Eugendorf]
  • A. Eugendorf chosen
    Eugendorf is a market town in the Austrian state of Salzburg, known for its proximity to the city of Salzburg and its location in the scenic Salzkammergut region.
  • B. Jägerndorf
    Jägerndorf is a historic Silesian town (now Krnov in the Czech Republic) known for its strategic and political significance in Central European history.
  • C. Hubersdorf
    Hubersdorf is a small municipality located in the canton of Solothurn in northwestern Switzerland.
  • D. Kiliansdorf
    Kiliansdorf is a village and district of the town of Roth in the Bavarian region of Germany.
  • E. Wintersdorf
    Wintersdorf is a village and district of the town of Rastatt in the state of Baden-Württemberg in southwestern Germany.
  • 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_69bd4462ed04819084fcb01eb9d2fa74 completed March 20, 2026, 12:58 p.m.
NER Named-entity recognition batch_69bd7a1f154481908be5d3c9cbbef92a completed March 20, 2026, 4:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69bfc9d6fb208190b81250ddbcd03b9b completed March 22, 2026, 10:52 a.m.
Created at: March 20, 2026, 1:47 p.m.