Triple

T4033137
Position Surface form Disambiguated ID Type / Status
Subject Harry Potter and the Half-Blood Prince (film) E83760 entity
Predicate editor P1954 FINISHED
Object Mark Day E117459 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: Mark Day | Statement: [Harry Potter and the Half-Blood Prince (film), editor, Mark Day]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mark Day
Context triple: [Harry Potter and the Half-Blood Prince (film), editor, Mark Day]
  • A. Mark Day chosen
    Mark Day is a British film editor best known for his long-time collaboration with director David Yates on several Harry Potter films and other major studio productions.
  • B. Timothy Day
    Timothy Day is an American flutist best known as the longtime principal flute of the San Francisco Symphony and a prominent orchestral performer and teacher.
  • C. Kenneth Dayes
    Kenneth Dayes is a musician best known as a member of the Jamaican roots reggae band Culture.
  • D. Ken Maynard
    Ken Maynard was a popular American silent- and early sound-era Western film star and stunt rider known for his roles as a cowboy hero.
  • E. Guy Day
    Guy Day was an American advertising executive best known as the co-founder of the influential agency Chiat/Day, which produced iconic campaigns such as Apple’s “1984” commercial.
  • 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_69aed92e29ac819080f7a98b594fec05 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefb108fc0819080c8f41da2e558e0 completed March 9, 2026, 4:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5563b6d8c8190862597ea39b56de1 completed March 14, 2026, 12:36 p.m.
Created at: March 9, 2026, 3:36 p.m.