Triple

T6201409
Position Surface form Disambiguated ID Type / Status
Subject Bezirk Rostock E138640 entity
Predicate contains P35 FINISHED
Object Rügen E23809 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: Rügen | Statement: [Bezirk Rostock, contains, Rügen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rügen
Context triple: [Bezirk Rostock, contains, 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. 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.
  • D. Bornholm
    Bornholm is a Danish island known for its rocky coastline, medieval ruins, and picturesque fishing villages in the Baltic Sea.
  • E. Norderney
    Norderney is a popular German North Sea island known for its sandy beaches, seaside resort town, and role as a major tourist destination in Lower Saxony.
  • 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_69c008acbea48190991c6b834bb45d65 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c062559bcc81908942bb4d25fe8158 completed March 22, 2026, 9:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69c67c36eac4819095b6a2b9e8a277f0 completed March 27, 2026, 12:46 p.m.
Created at: March 22, 2026, 4:20 p.m.