Triple

T12158240
Position Surface form Disambiguated ID Type / Status
Subject Wurzen E289633 entity
Predicate connectedByRailTo P848 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: [Wurzen, connectedByRailTo, Riesa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Riesa
Context triple: [Wurzen, connectedByRailTo, 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_69d6ab4c6710819097a9d228382dde43 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d915c277e481908351bf4e664dda42 completed April 10, 2026, 3:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69f65556e718819092736cd89c326fb5 completed May 2, 2026, 7:49 p.m.
Created at: April 8, 2026, 9:50 p.m.