Triple

T20787794
Position Surface form Disambiguated ID Type / Status
Subject VR E511685 entity
Predicate associatedWithIsland P27557 FINISHED
Object Rügen 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: Rügen | Statement: [VR, associatedWithIsland, Rügen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rügen
Context triple: [VR, associatedWithIsland, Rügen]
  • A. Rügen chosen
    Rügen is Germany’s largest island, known for its chalk cliffs, seaside resorts, and beaches along the Baltic Sea coast.
  • B. Hiddensee
    Hiddensee is a car-free German Baltic Sea island known for its unspoiled nature, sandy beaches, and role as a tranquil holiday destination west of Rügen.
  • C. Hiddensee
    Hiddensee is a novel by Gregory Maguire that reimagines the backstory of the Nutcracker and its mysterious creator in a dark, folkloric fantasy.
  • D. Island of Usedom
    The Island of Usedom is a Baltic Sea island shared by Germany and Poland, renowned for its long sandy beaches, seaside resorts, and status as a popular holiday destination.
  • E. Bornholm
    Bornholm is a Danish island known for its rocky coastline, medieval ruins, and picturesque fishing villages in the Baltic Sea.
  • 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_69e0b4cb83948190bd57bec21d78ed53 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c28d24708190bf3890a22d1ec4b7 completed April 21, 2026, 12:19 a.m.
Created at: April 16, 2026, 12:38 p.m.