Triple

T10885676
Position Surface form Disambiguated ID Type / Status
Subject Johan Söderqvist E257038 entity
Predicate name P16 FINISHED
Object Johan Söderqvist E257038 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: Johan Söderqvist | Statement: [Johan Söderqvist, name, Johan Söderqvist]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Johan Söderqvist
Context triple: [Johan Söderqvist, name, Johan Söderqvist]
  • A. Johan Söderqvist chosen
    Johan Söderqvist is a Swedish film composer known for his atmospheric and emotionally nuanced scores for Scandinavian and international cinema.
  • B. Göran Sonnevi
    Göran Sonnevi is a Swedish poet renowned for his intellectually dense, politically engaged, and formally experimental poetry.
  • C. Göran Månsson
    Göran Månsson is a Swedish architect best known for designing Stockholm’s renowned Vasa Museum, which houses the 17th-century warship Vasa.
  • D. Göran Andersson
    Göran Andersson is a Swedish academic and engineer known for his contributions to electric power systems and energy technology.
  • E. Torgny Segerstedt
    Torgny Segerstedt was a Swedish philosopher and academic leader best known for serving as rector of Uppsala University and for his influence on higher education in Sweden.
  • 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_69d6aa848804819081b2713ca0bedf06 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d751dd6a3c81909965ef774e8b7309 completed April 9, 2026, 7:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69e4f34d1e108190ad281dae6c92634e completed April 19, 2026, 3:22 p.m.
Created at: April 8, 2026, 9:21 p.m.