Triple

T12045481
Position Surface form Disambiguated ID Type / Status
Subject Anhalt E286774 entity
Predicate containsCity P294 FINISHED
Object Bernburg E506183 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: Bernburg | Statement: [Anhalt, containsCity, Bernburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bernburg
Context triple: [Anhalt, containsCity, Bernburg]
  • A. Bernburg chosen
    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.
  • B. Rudolstadt
    Rudolstadt is a historic town in the German state of Thuringia, known for its picturesque old town, Heidecksburg Castle, and cultural festivals.
  • C. Weiterstadt
    Weiterstadt is a town in the German state of Hesse, located near Darmstadt and known for its residential areas and commercial centers.
  • D. Wernigerode
    Wernigerode is a picturesque German town in Saxony-Anhalt known for its colorful half-timbered houses, medieval castle, and location on the northern slopes of the Harz Mountains.
  • E. Schleiz
    Schleiz is a historic town in eastern Thuringia, Germany, known for its role as a former princely residence and for the nearby Schleizer Dreieck motor racing circuit.
  • 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_69d6ab4780948190bdb9f7620c2ac27e completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d9041fe3b0819094b82a6b17ac59c3 completed April 10, 2026, 2:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6e2548a848190a3a72415a5e4d0fd completed May 3, 2026, 5:51 a.m.
Created at: April 8, 2026, 9:47 p.m.