Triple

T12333507
Position Surface form Disambiguated ID Type / Status
Subject Blow Dry E294021 entity
Predicate cinematographyBy P1953 FINISHED
Object Alexander Gruszynski E159607 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: Alexander Gruszynski | Statement: [Blow Dry, cinematographyBy, Alexander Gruszynski]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Alexander Gruszynski
Context triple: [Blow Dry, cinematographyBy, Alexander Gruszynski]
  • A. Alexander Gruszynski chosen
    Alexander Gruszynski is a Polish-born cinematographer and director known for his work on a variety of feature films and television projects.
  • B. Edward Szczepanik
    Edward Szczepanik was a Polish economist and politician who served as the last prime minister of the Polish government-in-exile before the restoration of democracy in Poland.
  • C. Jan Zaleski
    Jan Zaleski was a Polish biochemist known for his pioneering research in organic and physiological chemistry in the early 20th century.
  • D. Piotr Wysocki
    Piotr Wysocki was a Polish army officer and independence activist best known for initiating the November Uprising of 1830 against Russian rule.
  • E. Marek Zaleski
    Marek Zaleski is a Polish literary critic and essayist known for his work on modern Polish literature and literary theory.
  • 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_69d6ab6ae0dc8190b1522a9c1c55c114 completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d93f64ad20819080d99e57833b4b51 completed April 10, 2026, 6:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69f75d78433081909ae0278e9e1abacb completed May 3, 2026, 2:36 p.m.
Created at: April 8, 2026, 9:53 p.m.