Triple

T10559276
Position Surface form Disambiguated ID Type / Status
Subject Dinner Roll trick E249171 entity
Predicate popularizedBy P4586 FINISHED
Object Jonny Moseley E50012 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: Jonny Moseley | Statement: [Dinner Roll trick, popularizedBy, Jonny Moseley]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jonny Moseley
Context triple: [Dinner Roll trick, popularizedBy, Jonny Moseley]
  • A. Jonny Moseley chosen
    Jonny Moseley is an American freestyle skier best known for winning the gold medal in moguls and popularizing innovative tricks on the international stage.
  • B. Kyle Macy
    Kyle Macy is a former American college basketball star and NBA guard best known for his standout career at the University of Kentucky in the late 1970s.
  • C. George Gatins
    George Gatins is an American screenwriter and film producer best known for writing the 2014 action film adaptation of the racing video game series Need for Speed.
  • D. Brian Gilbert
    Brian Gilbert is a British film and television director known for works such as the biographical drama "Tom & Viv."
  • E. Bobby Newmyer
    Bobby Newmyer was an American film producer known for backing gritty, character-driven movies in Hollywood, including the crime thriller "Training Day."
  • 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_69d381c8bd708190acf3d275c908251e completed April 6, 2026, 9:50 a.m.
NER Named-entity recognition batch_69d5271e65688190bcf7931373d87f94 completed April 7, 2026, 3:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69d95e6e28a481909a90059e6ce51f6d completed April 10, 2026, 8:32 p.m.
Created at: April 6, 2026, 12:35 p.m.