Triple

T16178072
Position Surface form Disambiguated ID Type / Status
Subject Torsten N. Wiesel E392616 entity
Predicate placeOfBirth P1 FINISHED
Object Uppsala, Sweden E36359 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: Uppsala, Sweden | Statement: [Torsten N. Wiesel, placeOfBirth, Uppsala, Sweden]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Uppsala, Sweden
Context triple: [Torsten N. Wiesel, placeOfBirth, Uppsala, Sweden]
  • A. Uppsala chosen
    Uppsala is a historic Swedish city north of Stockholm, known for its prestigious university, medieval cathedral, and role as a cultural and ecclesiastical center.
  • B. Södertälje, Sweden
    Södertälje, Sweden is an industrial city southwest of Stockholm known for its major manufacturing plants, particularly in the automotive and heavy vehicle sectors.
  • C. Karlskoga, Sweden
    Karlskoga, Sweden is an industrial town in central Sweden best known for its historic arms manufacturer Bofors and its association with Alfred Nobel.
  • D. Lund
    Lund is a historic city in southern Sweden known for its medieval cathedral, prestigious university, and role as a significant cultural and academic center in Scandinavia.
  • E. Lund
    Lund is a district of the Norwegian city of Kristiansand, known for its residential areas, educational institutions, and proximity to the city center.
  • 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_69d87f1d32208190942e4e499a80c18c completed April 10, 2026, 4:39 a.m.
NER Named-entity recognition batch_69e2205ab3108190a84fc2dfe61d5044 completed April 17, 2026, 11:58 a.m.
NED1 Entity disambiguation (via context triple) batch_6a000787d3fc8190a32d53a177fedb6d completed May 10, 2026, 4:20 a.m.
Created at: April 10, 2026, 5:02 a.m.