Triple

T20578303
Position Surface form Disambiguated ID Type / Status
Subject Michael Shub E505281 entity
Predicate coAuthorWith P398 FINISHED
Object Manuel Blum 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: Manuel Blum | Statement: [Michael Shub, coAuthorWith, Manuel Blum]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Manuel Blum
Context triple: [Michael Shub, coAuthorWith, Manuel Blum]
  • A. Manuel Blum chosen
    Manuel Blum is a Venezuelan-American computer scientist and Turing Award laureate renowned for his foundational contributions to computational complexity theory and cryptography.
  • B. Eli Upfal
    Eli Upfal is a computer scientist known for his contributions to randomized algorithms, probabilistic analysis, and theoretical computer science.
  • C. Leslie Valiant
    Leslie Valiant is a renowned computer scientist known for his foundational work in computational learning theory, complexity theory, and artificial intelligence.
  • D. Michael Sipser
    Michael Sipser is an American theoretical computer scientist known for his influential work in computational complexity theory and for authoring a widely used textbook on the theory of computation.
  • E. Oded Goldreich
    Oded Goldreich is an Israeli computer scientist renowned for his foundational contributions to cryptography, computational complexity, and the theory of pseudorandomness.
  • 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_69e0b4b721588190993ac7b0a9be2736 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6a90cc22c8190969e3a21ae92f1c9 completed April 20, 2026, 10:30 p.m.
Created at: April 16, 2026, 11:39 a.m.