Triple

T14943970
Position Surface form Disambiguated ID Type / Status
Subject Soldier Blue E372604 entity
Predicate director P255 FINISHED
Object Ralph Nelson E6938 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: Ralph Nelson | Statement: [Soldier Blue, director, Ralph Nelson]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ralph Nelson
Context triple: [Soldier Blue, director, Ralph Nelson]
  • A. Ralph Nelson chosen
    Ralph Nelson was an American film and television director, producer, and writer known for works such as "Lilies of the Field" and "Requiem for a Heavyweight."
  • B. Herbert Reynolds
    Herbert Reynolds was an early 20th-century American lyricist best known for his collaborations with composer Jerome Kern on popular songs from the musical theatre and Tin Pan Alley era.
  • C. Robert Healey
    Robert Healey is a name shared by several notable individuals, including an American lawyer and perennial political candidate known for his satirical campaigns.
  • D. Lewis Arnold
    Lewis Arnold is a British television director known for his work on acclaimed drama series such as "Prey."
  • E. Joseph W. Young
    Joseph W. Young was an American real estate developer and city planner best known for creating and developing the planned community of Hollywood, Florida, in the early 20th century.
  • 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_69d85cc9da0c81908d583ca3f63a3908 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded68d20048190a403af85fe43dede completed April 15, 2026, 12:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffc3aeb59c8190a39ccb4df7815ed0 completed May 9, 2026, 11:30 p.m.
Created at: April 10, 2026, 2:38 a.m.