Triple

T1369064
Position Surface form Disambiguated ID Type / Status
Subject Günter Grass E30068 entity
Predicate residence P75 FINISHED
Object Lübeck E56916 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: Lübeck | Statement: [Günter Grass, residence, Lübeck]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lübeck
Context triple: [Günter Grass, residence, Lübeck]
  • A. Lübeck chosen
    Lübeck is a historic Hanseatic city in northern Germany renowned for its medieval architecture and long-standing role as a key trading hub on the Baltic Sea.
  • 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. Wismar
    Wismar is a historic Hanseatic port city on Germany’s Baltic Sea coast, known for its well-preserved medieval architecture and UNESCO-listed old town.
  • D. Bremen
    Bremen is a city-state in northwestern Germany comprising the cities of Bremen and Bremerhaven, known for its historic Hanseatic heritage and major port on the Weser River.
  • E. Schwerin
    Schwerin is a historic city in northern Germany known for its picturesque lakeside setting and landmark Schwerin Castle.
  • 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_69a498f912008190a376a98b207b2071 completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c2d60fdc8190a9954b74ca2b2541 completed March 1, 2026, 10:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69ae02f56b348190b71875b0c839c729 completed March 8, 2026, 11:15 p.m.
Created at: March 1, 2026, 7:57 p.m.