Triple

T8848366
Position Surface form Disambiguated ID Type / Status
Subject Naumburg E210567 entity
Predicate locatedIn P40 FINISHED
Object Saxony-Anhalt E48552 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: Saxony-Anhalt | Statement: [Naumburg, locatedIn, Saxony-Anhalt]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Saxony-Anhalt
Context triple: [Naumburg, locatedIn, Saxony-Anhalt]
  • A. Saxony-Anhalt chosen
    Saxony-Anhalt is a federal state in central Germany known for its rich cultural heritage, including numerous UNESCO World Heritage Sites such as the Bauhaus in Dessau and the historic towns of Quedlinburg and Wittenberg.
  • B. Thuringia
    Thuringia is a federal state in central Germany known for its forested landscapes, historic cities like Weimar and Erfurt, and its rich cultural and intellectual heritage.
  • C. Upper Saxony
    Upper Saxony is a historical region in central Europe that formed part of the Electorate of Saxony and encompassed areas including the city of Dresden.
  • D. Lower Saxony
    Lower Saxony is a large federal state in northwestern Germany known for its diverse landscapes, strong industrial base, and historic cities such as Hanover and Göttingen.
  • E. Mecklenburg-Vorpommern
    Mecklenburg-Vorpommern is a federal state in northeastern Germany known for its Baltic Sea coastline, numerous lakes, and relatively low population density.
  • 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_69ca838967bc8190b46c3c80a2887ea4 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc60aa6db0819097c3257499200afc completed April 1, 2026, 12:02 a.m.
NED1 Entity disambiguation (via context triple) batch_69cffd6b7eb88190b878165e41cf1df8 completed April 3, 2026, 5:48 p.m.
Created at: March 30, 2026, 6:49 p.m.