Triple

T5973439
Position Surface form Disambiguated ID Type / Status
Subject Lord High Treasurer of Sweden E132928 entity
Predicate officeHolder P537 FINISHED
Object Sten Bielke E187175 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: Sten Bielke | Statement: [Lord High Treasurer of Sweden, officeHolder, Sten Bielke]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sten Bielke
Context triple: [Lord High Treasurer of Sweden, officeHolder, Sten Bielke]
  • A. Ture Turesson Bielke
    Ture Turesson Bielke was a prominent 16th-century Swedish nobleman and statesman who played a key role in the political life of Sweden during the turbulent era of the Kalmar Union and early Vasa rule.
  • B. Stig Strömholm
    Stig Strömholm is a Swedish legal scholar, author, and academic leader who served as a prominent rector of Uppsala University.
  • C. Sten Carl Bielke chosen
    Sten Carl Bielke was an 18th-century Swedish statesman and scientist who played a key role in advancing scientific institutions in Sweden.
  • D. Ragnar Östberg
    Ragnar Östberg was a prominent Swedish architect best known for designing Stockholm City Hall and for his influential role in early 20th-century Nordic architecture.
  • E. Karl Sodersten
    Karl Sodersten is a film editor known for his work on the Australian psychological thriller "Lantana."
  • 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_69c0086deab081908550159ca23eec9b completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c04a01dd4081909097342afff31f9b completed March 22, 2026, 7:58 p.m.
NED1 Entity disambiguation (via context triple) batch_69c16e909f9c8190a78254d81437f404 completed March 23, 2026, 4:47 p.m.
Created at: March 22, 2026, 4:03 p.m.