Triple

T11498369
Position Surface form Disambiguated ID Type / Status
Subject Næstved E272601 entity
Predicate hasTwinTown P919 FINISHED
Object Kristiansand E138455 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: Kristiansand | Statement: [Næstved, hasTwinTown, Kristiansand]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kristiansand
Context triple: [Næstved, hasTwinTown, Kristiansand]
  • A. Kristiansand chosen
    Kristiansand is a coastal city in southern Norway known for its harbor, beaches, and role as a regional cultural and economic center.
  • B. Kristiansund
    Kristiansund is a coastal city in western Norway known for its historic clipfish industry, distinctive layout across several islands, and picturesque harbor.
  • C. Stavanger
    Stavanger is a coastal city in southwestern Norway known for its oil industry hub status, historic wooden houses, and proximity to natural attractions like the Lysefjord and Preikestolen.
  • D. Egersund
    Egersund is a coastal town in southwestern Norway known for its fishing industry, historic wooden architecture, and scenic harbor.
  • E. Skien
    Skien is a historic city in southern Norway known as the birthplace of playwright Henrik Ibsen and as a regional commercial and industrial center.
  • 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_69d6aae1b09881909ce2ded3fa0c14fa completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d85de27db081909ccdb4ab0ef75bdb completed April 10, 2026, 2:18 a.m.
NED1 Entity disambiguation (via context triple) batch_69f6554ec69c8190ba9ffbaf220c44f4 completed May 2, 2026, 7:49 p.m.
Created at: April 8, 2026, 9:36 p.m.