Triple

T7298188
Position Surface form Disambiguated ID Type / Status
Subject Tears in Heaven E167776 entity
Predicate writer P1360 FINISHED
Object Will Jennings E164443 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: Will Jennings | Statement: [Tears in Heaven, writer, Will Jennings]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Will Jennings
Context triple: [Tears in Heaven, writer, Will Jennings]
  • A. Will Jennings chosen
    Will Jennings is an American songwriter and lyricist best known for penning the lyrics to numerous hit songs, including the Oscar-winning "My Heart Will Go On" from Titanic.
  • B. Don James
    Don James was a highly successful American college football coach best known for leading the University of Washington Huskies to national prominence, including a share of the 1991 national championship.
  • C. Sam O'Steen
    Sam O'Steen was an acclaimed American film editor best known for his work on influential films such as "The Graduate," "Chinatown," and "Cool Hand Luke."
  • D. Bo Welch
    Bo Welch is an American production designer and film director known for his imaginative visual work on films such as "Beetlejuice," "Edward Scissorhands," and "Men in Black."
  • E. Dan Moore
    Dan Moore is a fictional character appearing in the work "Cane."
  • 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_69c6888c820881909fc68f689fe1c251 completed March 27, 2026, 1:39 p.m.
NER Named-entity recognition batch_69c6eb8f83c881909e8eae85410f9659 completed March 27, 2026, 8:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69c83c43f24881908a434773dfd79892 completed March 28, 2026, 8:38 p.m.
Created at: March 27, 2026, 3 p.m.