Triple

T13011875
Position Surface form Disambiguated ID Type / Status
Subject Melissa Garner Wylie E322435 entity
Predicate relative P37 FINISHED
Object William John Garner E939636 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: William John Garner | Statement: [Melissa Garner Wylie, relative, William John Garner]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: William John Garner
Context triple: [Melissa Garner Wylie, relative, William John Garner]
  • A. William John Garner chosen
    William John Garner is the father of American actress Jennifer Garner and a former chemical engineer.
  • B. John Work Garrett
    John Work Garrett was a prominent 19th-century American banker and railroad executive who served as president of the Baltimore and Ohio Railroad and played a key role in its expansion and in Civil War logistics.
  • C. Roy Gardner
    Roy Gardner was an American economist and game theorist known for his work on strategic behavior, common-pool resources, and the application of game theory to environmental and resource economics.
  • D. Fred Gardner
    Fred Gardner is a writer best known for co-writing the screenplay of Michelangelo Antonioni’s 1970 counterculture film "Zabriskie Point."
  • E. Hugh Garner
    Hugh Garner was a Canadian author best known for his socially conscious novels and short stories depicting working-class life in Toronto.
  • 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_69d807657e8c8190bd9435ee2f823845 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d97e9e14b88190a2cee8e0c9bf31c8 completed April 10, 2026, 10:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6c10d5b9881909db688c1ab0e6a77 completed May 3, 2026, 3:29 a.m.
Created at: April 9, 2026, 8:49 p.m.