Triple

T6417649
Position Surface form Disambiguated ID Type / Status
Subject Hiddensee E127869 entity
Predicate administrativeSeat P21613 FINISHED
Object Vitte E591754 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: Vitte | Statement: [Hiddensee, administrativeSeat, Vitte]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vitte
Context triple: [Hiddensee, administrativeSeat, Vitte]
  • A. Vitte chosen
    Vitte is the largest village and main tourist and administrative center on the Baltic Sea island of Hiddensee in Germany.
  • B. Veitvet
    Veitvet is a residential neighborhood in Oslo, Norway, known for its apartment blocks, local shopping center, and multicultural community.
  • C. Svaneke
    Svaneke is a picturesque coastal town on the Danish island of Bornholm, known for its well-preserved half-timbered houses, harbor, and traditional smokehouses.
  • D. Wallesau
    Wallesau is a village-level subdivision of the town of Roth in the Bavarian region of Germany.
  • E. Vitasta
    Vitasta is the ancient Sanskrit name for the Jhelum River, a historically significant river of the Kashmir region frequently mentioned in Vedic and classical Indian texts.
  • 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_69c0083815208190a9b299b8e0640218 completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c068ea06b08190901e0c0a18fd5170 completed March 22, 2026, 10:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69c64bb79f74819088a6e30a9deb8616 completed March 27, 2026, 9:19 a.m.
Created at: March 22, 2026, 4:42 p.m.