Triple

T5634693
Position Surface form Disambiguated ID Type / Status
Subject Älvkarleby Municipality E147918 entity
Predicate seat P75 FINISHED
Object Älvkarleby E147918 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: Älvkarleby | Statement: [Älvkarleby Municipality, seat, Älvkarleby]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Älvkarleby
Context triple: [Älvkarleby Municipality, seat, Älvkarleby]
  • A. Tärnsjö
    Tärnsjö is a small locality in central Sweden known for its rural setting and traditional leather tanning industry.
  • B. Älvkarleby Municipality chosen
    Älvkarleby Municipality is a local government area in east-central Sweden known for its hydroelectric power production and scenic location along the Dalälven River.
  • C. Strängnäs
    Strängnäs is a historic Swedish town known for its medieval cathedral and picturesque location on the shores of Lake Mälaren.
  • D. Kårsta
    Kårsta is a locality in Vallentuna Municipality, Sweden, known as the northern terminus of Stockholm’s Roslagsbanan narrow-gauge railway line.
  • E. Bollnäs
    Bollnäs is a small Swedish town known for its scenic lakeside setting, traditional wooden architecture, and strong bandy sports culture.
  • 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_69c00907bc8881909ed760d3ed73ef35 completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c0226118548190877793dadf6cacba completed March 22, 2026, 5:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69c07dae1744819083cc8827ade7478c completed March 22, 2026, 11:39 p.m.
Created at: March 22, 2026, 3:41 p.m.