Triple

T1654778
Position Surface form Disambiguated ID Type / Status
Subject Saalekreis E35772 entity
Predicate administrativeSeat P21613 FINISHED
Object Merseburg E213906 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: Merseburg | Statement: [Saalekreis, administrativeSeat, Merseburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Merseburg
Context triple: [Saalekreis, administrativeSeat, Merseburg]
  • A. Merseburg chosen
    Merseburg is a historic town in the German state of Saxony-Anhalt, known for its medieval cathedral and role as an important cultural and administrative center on the River Saale.
  • B. Hildesheim
    Hildesheim is a historic city in northern Germany renowned for its medieval architecture and UNESCO-listed Romanesque churches.
  • C. Fulda
    Fulda is a historic city in central Germany known for its Baroque architecture and former status as an important monastic and ecclesiastical center.
  • D. Naumburg
    Naumburg is a historic town in the German state of Saxony-Anhalt, known for its medieval cathedral and as the childhood home of philosopher Friedrich Nietzsche.
  • E. Bamberg
    Bamberg is a historic city in northern Bavaria, Germany, renowned for its well-preserved medieval old town and status as a UNESCO World Heritage Site.
  • 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_69a8860568888190a32cd9f70acbba42 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a90a8b597c81908a62b41718d85df6 completed March 5, 2026, 4:46 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae26e9748081909532426be2f198d1 completed March 9, 2026, 1:48 a.m.
Created at: March 4, 2026, 7:29 p.m.