Triple

T18332695
Position Surface form Disambiguated ID Type / Status
Subject Åkerlund & Rausing E439184 entity
Predicate namedAfter P63 FINISHED
Object Ruben Rausing NE NERFINISHED

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: Ruben Rausing | Statement: [Åkerlund & Rausing, namedAfter, Ruben Rausing]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ruben Rausing
Context triple: [Åkerlund & Rausing, namedAfter, Ruben Rausing]
  • A. Ruben Rausing chosen
    Ruben Rausing was a Swedish industrialist and entrepreneur best known as the founder of Tetra Pak, the pioneering food packaging company that revolutionized liquid food distribution worldwide.
  • B. F. Ross Johnson
    F. Ross Johnson was a Canadian-American businessman best known as the high-profile CEO who led the leveraged buyout battle for RJR Nabisco in the late 1980s.
  • C. A. W. Ross
    A. W. Ross was a real estate developer best known for transforming Los Angeles’s Wilshire Boulevard into the bustling Miracle Mile commercial district.
  • D. Albert Hahl
    Albert Hahl was a German colonial administrator best known for serving as governor of German New Guinea in the early 20th century.
  • E. Aaron Lufkin Dennison
    Aaron Lufkin Dennison was a pioneering American watchmaker and industrialist who helped establish large-scale, standardized watch manufacturing in the United States.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8b9175fec8190af865699b4e64d8c completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e50ecaf6f48190ae7547cc0f8e6efa completed April 19, 2026, 5:20 p.m.
Created at: April 10, 2026, 10:36 a.m.