Triple

T12644242
Position Surface form Disambiguated ID Type / Status
Subject Montgomery Brewster E301978 entity
Predicate createdBy P806 FINISHED
Object George Barr McCutcheon E247183 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: George Barr McCutcheon | Statement: [Montgomery Brewster, createdBy, George Barr McCutcheon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: George Barr McCutcheon
Context triple: [Montgomery Brewster, createdBy, George Barr McCutcheon]
  • A. George Barr McCutcheon chosen
    George Barr McCutcheon was an American novelist best known for his popular early 20th-century works such as the novel "Brewster's Millions," which inspired numerous film adaptations.
  • B. Thomas Burke
    Thomas Burke was an American sprinter who became the first Olympic champion in both the 100-meter and 400-meter races at the modern Games.
  • C. Thomas Burke
    Thomas Burke was an American politician who served as the third Governor of North Carolina during the early years of the United States.
  • D. Thomas Burke
    Thomas Burke was a British author best known for his early 20th-century stories set in London’s East End, including the tale that inspired the film "Broken Blossoms."
  • E. Thomas Burke
    Thomas Burke is a common personal name shared by numerous individuals across various fields, including politics, sports, literature, and the arts.
  • 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_69d7bdec9f9c8190b4bac675b7588211 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d9614bf2f881909976becdf747f4fb completed April 10, 2026, 8:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69f67c71482c819083d65c1a39e90e41 completed May 2, 2026, 10:36 p.m.
Created at: April 9, 2026, 5:17 p.m.