Triple

T11588708
Position Surface form Disambiguated ID Type / Status
Subject Kelly Pegula E274821 entity
Predicate hasRelative P367 FINISHED
Object Jessica Pegula E935268 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: Jessica Pegula | Statement: [Kelly Pegula, hasRelative, Jessica Pegula]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jessica Pegula
Context triple: [Kelly Pegula, hasRelative, Jessica Pegula]
  • A. Jessica Pegula chosen
    Jessica Pegula is an American professional tennis player known for her success on the WTA Tour and for being one of the top-ranked women’s singles competitors in the world.
  • B. Laura Pegula
    Laura Pegula is a member of the Pegula family, known for their prominence in American professional sports and business through ownership stakes in major franchises.
  • C. Sloane Stephens
    Sloane Stephens is an American professional tennis player best known for winning the 2017 US Open singles title and reaching a career-high ranking inside the WTA top 5.
  • D. Kelly Pegula
    Kelly Pegula is a member of the Pegula family, known for their prominent role in American professional sports ownership and business.
  • E. Alexa Kenin
    Alexa Kenin was an American film and television actress known for her supporting roles in 1980s movies such as "Pretty in Pink" and "Little Darlings."
  • 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_69d6aae6b14c81908dc5a74bad7591f9 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d89463360c8190b91228c46bfe2e5f completed April 10, 2026, 6:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69ee86df14048190ae8f6d8b11610079 completed April 26, 2026, 9:42 p.m.
Created at: April 8, 2026, 9:38 p.m.