Triple

T19430668
Position Surface form Disambiguated ID Type / Status
Subject Leon Jaworski E486100 entity
Predicate child P120 FINISHED
Object Joseph Jaworski (son) 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: Joseph Jaworski (son) | Statement: [Leon Jaworski, child, Joseph Jaworski (son)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Joseph Jaworski (son)
Context triple: [Leon Jaworski, child, Joseph Jaworski (son)]
  • A. Joseph Jaworski (son) chosen
    Joseph Jaworski (son) is known primarily as the son of Leon Jaworski, the prominent Watergate special prosecutor and American attorney.
  • B. John Lesinski Jr.
    John Lesinski Jr. was an American Democratic politician who served as a U.S. Representative from Michigan in the mid-20th century.
  • C. Greg Joswiak
    Greg Joswiak is a senior Apple executive who serves as the company’s Senior Vice President of Worldwide Marketing.
  • D. Stephen Kazmierski
    Stephen Kazmierski is a cinematographer best known for his work on the acclaimed independent film "You Can Count on Me."
  • E. Joseph Jaworski
    Joseph Jaworski is an American organizational learning theorist, leadership consultant, and author known for his work on transformational leadership and the concept of "synchronicity" in personal and organizational change.
  • 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_69d8e8d688f881909c85104a62e09d8a completed April 10, 2026, 12:11 p.m.
NER Named-entity recognition batch_69e6321c774881908ba5bde20abd5adc completed April 20, 2026, 2:03 p.m.
Created at: April 10, 2026, 1:37 p.m.