Triple

T13642620
Position Surface form Disambiguated ID Type / Status
Subject Sunshine in Their Eyes E326020 entity
Predicate producer P490 FINISHED
Object Malcolm Cecil E325976 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: Malcolm Cecil | Statement: [Sunshine in Their Eyes, producer, Malcolm Cecil]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Malcolm Cecil
Context triple: [Sunshine in Their Eyes, producer, Malcolm Cecil]
  • A. Malcolm Cecil chosen
    Malcolm Cecil was a pioneering British musician, producer, and electronic music innovator best known for co-creating the massive TONTO synthesizer and shaping the sound of early 1970s Stevie Wonder albums.
  • B. Malcolm McDonald
    Malcolm McDonald is a British marketing scholar and author renowned for his influential work on marketing planning and strategy.
  • C. George Morrison
    George Morrison is a notable individual whose name is shared with several prominent figures across fields such as politics, art, and entertainment.
  • D. Roy Marples
    Roy Marples is a software engineer best known for his work on the OpenRC init system and various networking tools in the Linux and BSD ecosystems.
  • E. Malcolm Cooke
    Malcolm Cooke is a film editor known for his work on the 1986 monster movie "King Kong Lives."
  • 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_69d8076beddc8190a53156f5bea77f5e completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbc5ac2af88190976abe6606994eef completed April 12, 2026, 4:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7943306f08190b3a4c44e5b22db0a completed May 3, 2026, 6:30 p.m.
Created at: April 9, 2026, 9:51 p.m.