Triple

T16777015
Position Surface form Disambiguated ID Type / Status
Subject House of Saxe-Saalfeld E407750 entity
Predicate associatedWithTerritory P12445 FINISHED
Object Saalfeld E224220 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: Saalfeld | Statement: [House of Saxe-Saalfeld, associatedWithTerritory, Saalfeld]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Saalfeld
Context triple: [House of Saxe-Saalfeld, associatedWithTerritory, Saalfeld]
  • A. Saalfeld chosen
    Saalfeld is a town in the German state of Thuringia, known for its historic old town and former significance as a regional railway and industrial center.
  • B. Suhl
    Suhl is a city in central Germany known historically as a center of firearms manufacturing and located in the federal state of Thuringia.
  • C. Schmalkalden
    Schmalkalden is a historic town in the German state of Thuringia, known for its well-preserved medieval architecture and role in Reformation-era politics.
  • D. Kronach
    Kronach is a historic town in northern Bavaria, Germany, known for its well-preserved medieval old town and the imposing Rosenberg Fortress.
  • E. Ludwigsstadt
    Ludwigsstadt is a small town in northern Bavaria, Germany, known for its location in the Franconian Forest near the Thuringian border.
  • 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_69d8839270588190886720d9519bbf8f completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e3b03a646c8190b3944c9f0c25af27 completed April 18, 2026, 4:24 p.m.
NED1 Entity disambiguation (via context triple) batch_6a01673ba0e08190a988847fa7ad8e50 completed May 11, 2026, 5:20 a.m.
Created at: April 10, 2026, 5:22 a.m.