Triple

T18151690
Position Surface form Disambiguated ID Type / Status
Subject House of Saxe-Lauenburg E434516 entity
Predicate hasSeat P3522 FINISHED
Object Ratzeburg 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: Ratzeburg | Statement: [House of Saxe-Lauenburg, hasSeat, Ratzeburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ratzeburg
Context triple: [House of Saxe-Lauenburg, hasSeat, Ratzeburg]
  • A. Ratzeburg chosen
    Ratzeburg is a historic town in northern Germany known for its island old town and Romanesque cathedral, situated in the lake district of Schleswig-Holstein.
  • B. Rostock
    Rostock is a historic Hanseatic city in northern Germany known for its significant seaport on the Baltic Sea and its long maritime and trading tradition.
  • C. Neubrandenburg
    Neubrandenburg is a historic city in northeastern Germany known for its well-preserved medieval brick Gothic architecture and distinctive city wall with multiple gate towers.
  • D. Schwerin
    Schwerin is a historic city in northern Germany known for its picturesque lakeside setting and landmark Schwerin Castle.
  • E. Magdeburg
    Magdeburg is a historic city in central Germany, known for its medieval cathedral, role as a major trading and industrial center, and location on the Elbe River.
  • 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_69d8b90aac308190801e2c57d8c5bfe5 completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4de38d4e08190bc4d430b70b7e288 completed April 19, 2026, 1:52 p.m.
Created at: April 10, 2026, 10:29 a.m.