Triple

T14641899
Position Surface form Disambiguated ID Type / Status
Subject Perelman E343744 entity
Predicate hasNotableBearer P458 FINISHED
Object Raymond G. Perelman E69157 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: Raymond G. Perelman | Statement: [Perelman, hasNotableBearer, Raymond G. Perelman]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Raymond G. Perelman
Context triple: [Perelman, hasNotableBearer, Raymond G. Perelman]
  • A. Raymond G. Perelman chosen
    Raymond G. Perelman was an American businessman and philanthropist known for his major charitable contributions to education, medicine, and the arts, particularly at the University of Pennsylvania.
  • B. Kenneth Posner
    Kenneth Posner is a prominent American theatrical lighting designer known for his work on numerous Broadway productions.
  • C. Roy R. Neuberger
    Roy R. Neuberger was an American financier, philanthropist, and prominent modern art collector who played a key role in supporting and promoting 20th-century artists.
  • D. Daniel H. Lowenstein
    Daniel H. Lowenstein is a prominent American legal scholar known for his pioneering work in election law and political reform.
  • E. Milton J. Rubenstein
    Milton J. Rubenstein was a philanthropist and prominent supporter of science and technology education, for whom the Museum of Science and Technology in Syracuse, New York is named.
  • 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_69d822e1a2cc81908e5bb93cf61ce3cc completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb4e80aa48190884bab800f357106 completed April 14, 2026, 9:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69fdd5d404e881908d26e684702ae122 completed May 8, 2026, 12:23 p.m.
Created at: April 10, 2026, 1:26 a.m.