Triple

T9975493
Position Surface form Disambiguated ID Type / Status
Subject Göhren E196316 entity
Predicate locatedOn P40 FINISHED
Object island of 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: island of Rügen | Statement: [Göhren, locatedOn, island of Rügen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: island of Rügen
Context triple: [Göhren, locatedOn, island of 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. 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.
  • C. 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.
  • D. Sasseninsel
    Sasseninsel is a small island located in the Eibsee, a picturesque alpine lake near the Zugspitze in Bavaria, Germany.
  • 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_69ca82eea2b88190a0e511d21a31f386 completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cdb84b47308190aa2f94fa7320cdc3 completed April 2, 2026, 12:28 a.m.
NED1 Entity disambiguation (via context triple) batch_69d23ddcc01c8190bb00ac13cbbbb1fb completed April 5, 2026, 10:47 a.m.
Created at: March 30, 2026, 8:48 p.m.