Triple

T11500459
Position Surface form Disambiguated ID Type / Status
Subject Riesa region E272650 entity
Predicate hasCenter P35 FINISHED
Object Riesa E380063 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: Riesa | Statement: [Riesa region, hasCenter, Riesa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Riesa
Context triple: [Riesa region, hasCenter, Riesa]
  • A. Riesa chosen
    Riesa is a town in the German state of Saxony, situated on the Elbe River and known historically as an important regional railway and industrial center.
  • B. Bischofswerda
    Bischofswerda is a small town in the Saxony region of eastern Germany, known as a local commercial and transport hub near the city of Dresden.
  • C. Bautzen
    Bautzen is a historic town in eastern Germany known for its well-preserved medieval architecture and as a cultural center of the Sorbian minority.
  • D. Neukieritzsch
    Neukieritzsch is a small municipality in the German state of Saxony, situated south of Leipzig and known historically as a local railway junction.
  • E. Zinnowitz
    Zinnowitz is a seaside resort town on Germany’s Baltic Sea coast, known for its sandy beaches, historic spa architecture, and tourism on the island of Usedom.
  • 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_69d6aae1b09881909ce2ded3fa0c14fa completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d85de3e9c881909d6c55334f7a832d completed April 10, 2026, 2:18 a.m.
NED1 Entity disambiguation (via context triple) batch_69f489dfb2c881908a6f6bcd8b2d1cdc completed May 1, 2026, 11:09 a.m.
Created at: April 8, 2026, 9:36 p.m.