Triple

T18731355
Position Surface form Disambiguated ID Type / Status
Subject Sunshine Cleaning E458041 entity
Predicate cinematographer P1953 FINISHED
Object Jerzy Zieliński 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: Jerzy Zieliński | Statement: [Sunshine Cleaning, cinematographer, Jerzy Zieliński]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jerzy Zieliński
Context triple: [Sunshine Cleaning, cinematographer, Jerzy Zieliński]
  • A. Jerzy Zieliński chosen
    Jerzy Zieliński is a Polish cinematographer known for his work on both European and Hollywood films, including the dark comedy thriller "Teaching Mrs. Tingle."
  • B. Grzegorz Napieralski
    Grzegorz Napieralski is a Polish left-wing politician who served as a prominent leader of the Democratic Left Alliance and a member of the Polish parliament and European Parliament.
  • C. Krzysztof Zdzitowiecki
    Krzysztof Zdzitowiecki is a Polish mountaineer known for participating in the first ascent of the Himalayan peak Gasherbrum III.
  • D. Grzegorz Lato
    Grzegorz Lato is a former Polish footballer renowned as one of Poland’s greatest forwards, celebrated for his prolific scoring and key role in the national team’s successes in the 1970s.
  • E. Piotr Wolski
    Piotr Wolski is a researcher known for co-authoring scientific work with machine learning scientist Marcin Andrychowicz.
  • 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_69d8d393ba9c8190a8b03b04ddbb0a09 completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e56d7854748190b66c4aaadfd67f29 completed April 20, 2026, 12:04 a.m.
Created at: April 10, 2026, 11:51 a.m.