Triple

T10165167
Position Surface form Disambiguated ID Type / Status
Subject Bishop of Strängnäs E235187 entity
Predicate seeCity P3207 FINISHED
Object Strängnäs E176028 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: Strängnäs | Statement: [Bishop of Strängnäs, seeCity, Strängnäs]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Strängnäs
Context triple: [Bishop of Strängnäs, seeCity, Strängnäs]
  • A. Strängnäs chosen
    Strängnäs is a historic Swedish town known for its medieval cathedral and picturesque location on the shores of Lake Mälaren.
  • B. Nässjö
    Nässjö is a small Swedish town in Jönköping County known as a regional railway hub and service center in southern Sweden.
  • C. Hållnäs
    Hållnäs is a rural coastal locality and peninsula in eastern Sweden known for its forests, farmland, and Baltic Sea shoreline within Tierp Municipality in Uppsala County.
  • D. Tärnsjö
    Tärnsjö is a small locality in central Sweden known for its rural setting and traditional leather tanning industry.
  • E. Nykvarn
    Nykvarn is a small locality in eastern Sweden that serves as the administrative and population center of Nykvarn Municipality in Stockholm County.
  • 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_69ca84ceafd0819085828600e11bed6b completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cdec6b96dc8190ae37d0d28e4c393b completed April 2, 2026, 4:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69d300d672fc8190ad5b937d02a737fd completed April 6, 2026, 12:39 a.m.
Created at: March 30, 2026, 9:10 p.m.