Triple

T5652463
Position Surface form Disambiguated ID Type / Status
Subject Norwegian railway network E124536 entity
Predicate connectsCity P4245 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: [Norwegian railway network, connectsCity, Kristiansand]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kristiansand
Context triple: [Norwegian railway network, connectsCity, 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. 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.
  • E. Sandefjord
    Sandefjord is a coastal town and municipality in southern Norway known for its maritime heritage, whaling history, and popular seaside attractions.
  • 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_69c00825df388190a58742fa9b1aa33d completed March 22, 2026, 3:17 p.m.
NER Named-entity recognition batch_69c022d8a2588190b10de59edbc8841f completed March 22, 2026, 5:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69c16e69e9188190a4dd94c34657a74f completed March 23, 2026, 4:46 p.m.
Created at: March 22, 2026, 3:42 p.m.