Triple

T17864861
Position Surface form Disambiguated ID Type / Status
Subject Jim Menzies E446667 entity
Predicate name P16 FINISHED
Object Jim Menzies NE NERFINISHED

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: Jim Menzies | Statement: [Jim Menzies, name, Jim Menzies]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jim Menzies
Context triple: [Jim Menzies, name, Jim Menzies]
  • A. Jim Menzies chosen
    Jim Menzies is a person notable enough to be recognized as a prominent bearer of the surname Menzies.
  • B. John Fraser
    John Fraser was a Scottish actor known for his roles in British cinema and television from the 1950s onward.
  • C. Thomas McKay
    Thomas McKay was a 19th-century Scottish-born Canadian stonemason, entrepreneur, and early industrialist who played a key role in the development of Ottawa.
  • D. Johnny Stuart
    Johnny Stuart is the adventurous young boy who befriends the friendly sea monster Sigmund in the 1970s children's television series "Sigmund and the Sea Monsters."
  • E. Jock Sutherland
    Jock Sutherland was a prominent early 20th-century American football coach best known for his successful tenure at the University of Pittsburgh and later in the NFL.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8b9f4c22c819093c2680434472894 completed April 10, 2026, 8:51 a.m.
NER Named-entity recognition batch_69e49792ccf88190a0984963bb385688 completed April 19, 2026, 8:51 a.m.
Created at: April 10, 2026, 10:17 a.m.