Triple

T13184267
Position Surface form Disambiguated ID Type / Status
Subject Progressive E313806 entity
Predicate foundedBy P104 FINISHED
Object Joseph Lewis E761836 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: Joseph Lewis | Statement: [Progressive, foundedBy, Joseph Lewis]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Joseph Lewis
Context triple: [Progressive, foundedBy, Joseph Lewis]
  • A. Joseph Lewis chosen
    Joseph Lewis was an American businessman best known as a co-founder of the Progressive Corporation, one of the largest auto insurance companies in the United States.
  • B. Frankie Elkin
    Frankie Elkin is a recurring protagonist in Lisa Gardner’s crime novels, known as an ordinary yet relentless woman who dedicates her life to finding missing people that the world has forgotten.
  • C. Joseph W. Young
    Joseph W. Young was an American real estate developer and city planner best known for creating and developing the planned community of Hollywood, Florida, in the early 20th century.
  • D. Lewis Allen
    Lewis Allen was a local figure of significance after whom the city of Allen Park, Michigan, was named.
  • E. Lewis Allen
    Lewis Allen was a British-born film and television director best known for his atmospheric work in mid-20th-century Hollywood cinema.
  • 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_69d806ae1e08819090d95bfe1538cc17 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d98c4a0b0081908027bf77442ff5ff completed April 10, 2026, 11:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6f5f307408190afd0df16a417c456 completed May 3, 2026, 7:14 a.m.
Created at: April 9, 2026, 9:15 p.m.