Triple

T16226961
Position Surface form Disambiguated ID Type / Status
Subject Principality of Schwarzburg E393874 entity
Predicate capital P234 FINISHED
Object Rudolstadt NE NERFINISHED

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: Rudolstadt | Statement: [Principality of Schwarzburg, capital, Rudolstadt]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rudolstadt
Context triple: [Principality of Schwarzburg, capital, Rudolstadt]
  • A. Rudolstadt chosen
    Rudolstadt is a historic town in the German state of Thuringia, known for its picturesque old town, Heidecksburg Castle, and cultural festivals.
  • B. Weiterstadt
    Weiterstadt is a town in the German state of Hesse, located near Darmstadt and known for its residential areas and commercial centers.
  • C. Bernburg
    Bernburg is a town in the German state of Saxony-Anhalt, historically known for its castle overlooking the Saale River and its role as an industrial and cultural center in the region.
  • D. Weißenfels
    Weißenfels is a historic town in the German state of Saxony-Anhalt, known for its baroque architecture and former prominence as a ducal residence and industrial center.
  • E. Haldensleben
    Haldensleben is a town in the German state of Saxony-Anhalt, known as an administrative and economic center with historical roots dating back to the Middle Ages.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d87f204df88190a8f88923decf9835 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e23d26b02c819080b70ab7cc3bcc24 completed April 17, 2026, 2:01 p.m.
Created at: April 10, 2026, 5:03 a.m.