Triple

T9975730
Position Surface form Disambiguated ID Type / Status
Subject Hudiksvall Municipality E196321 entity
Predicate hasUrbanArea P316 FINISHED
Object Iggesund E834047 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: Iggesund | Statement: [Hudiksvall Municipality, hasUrbanArea, Iggesund]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Iggesund
Context triple: [Hudiksvall Municipality, hasUrbanArea, Iggesund]
  • A. Iggesund chosen
    Iggesund is a small industrial locality in Gävleborg County, Sweden, known for its paperboard mill and proximity to the Gulf of Bothnia.
  • B. Ginnerup
    Ginnerup is a small village in Denmark best known as the birthplace of former Danish Prime Minister and NATO Secretary General Anders Fogh Rasmussen.
  • C. Solvang
    Solvang is a Danish-themed tourist town in California known for its Scandinavian architecture, bakeries, and wineries.
  • D. Nørholm
    Nørholm is a historic estate in southern Norway best known as the longtime home of Nobel Prize–winning author Knut Hamsun (born Knut Pedersen).
  • E. Farsund
    Farsund is a coastal town and municipality in southern Norway known for its maritime heritage, beaches, and historic wooden architecture.
  • 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_69d281ebf6f88190a94ba8231422c591 completed April 5, 2026, 3:38 p.m.
Created at: March 30, 2026, 8:48 p.m.