Triple

T21383533
Position Surface form Disambiguated ID Type / Status
Subject Białogard E527426 entity
Predicate hasTwinTown P919 FINISHED
Object Söderhamn NE NERFINISHED

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: Söderhamn | Statement: [Białogard, hasTwinTown, Söderhamn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Söderhamn
Context triple: [Białogard, hasTwinTown, Söderhamn]
  • A. Söderhamn chosen
    Söderhamn is a coastal town in east-central Sweden known for its historical wooden architecture and role as the administrative and commercial center of the surrounding region.
  • B. Hässleholm
    Hässleholm is a town in southern Sweden’s Skåne County known as a regional railway hub and service center.
  • C. Hammarö
    Hammarö is a Swedish island and municipality in Värmland County, known for its forests, coastline, and proximity to the city of Karlstad.
  • D. Oskarshamn
    Oskarshamn is a coastal town in southeastern Sweden known for its Baltic Sea harbor and proximity to the island of Gotland.
  • E. Fredrikshamn
    Fredrikshamn (Hamina) is a coastal town in southeastern Finland that historically served as an important military and trading center.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b51f363c8190944000ab5523b02b completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e8b0f05278819096c511035ffc9777 completed April 22, 2026, 11:28 a.m.
Created at: April 16, 2026, 5:12 p.m.