Triple

T16811984
Position Surface form Disambiguated ID Type / Status
Subject Vestre Toten E408636 entity
Predicate partOf P40 FINISHED
Object Toten district E1062783 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: Toten district | Statement: [Vestre Toten, partOf, Toten district]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Toten district
Context triple: [Vestre Toten, partOf, Toten district]
  • A. Toten district chosen
    Toten district is a traditional agricultural region in Innlandet county, Norway, known for its rolling farmland and rural communities.
  • B. Uithof district
    The Uithof district is a major academic and research area in Utrecht, Netherlands, known for hosting university buildings, student housing, and scientific institutions.
  • C. Regen District
    Regen District is an administrative district in the Bavarian Forest region of Bavaria, Germany, known for its wooded landscapes and proximity to the Czech border.
  • D. Bezuidenhout district
    Bezuidenhout district is a central neighborhood in The Hague, Netherlands, known for its mix of offices, residential areas, and major transport connections.
  • E. De Wijk
    De Wijk is a village in the Dutch province of Drenthe, known for its historic buildings and rural character within the municipality of De Wolden.
  • 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_69d88393905081908d00a86b99996ac8 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e3b2d0793c81909d938ac174a6e63a completed April 18, 2026, 4:35 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00bb0f863081908e74dc4a7c91e91d completed May 10, 2026, 5:06 p.m.
Created at: April 10, 2026, 5:23 a.m.