Triple

T22423509
Position Surface form Disambiguated ID Type / Status
Subject Erdős–Rényi law of large numbers E554306 entity
Predicate developedBy P73 FINISHED
Object Alfréd Rényi E40505 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: Alfréd Rényi | Statement: [Erdős–Rényi law of large numbers, developedBy, Alfréd Rényi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Alfréd Rényi
Context triple: [Erdős–Rényi law of large numbers, developedBy, Alfréd Rényi]
  • A. Alfréd Rényi chosen
    Alfréd Rényi was a Hungarian mathematician renowned for his influential work in probability theory, information theory, and number theory.
  • B. Pál Turán
    Pál Turán was a Hungarian mathematician renowned for his influential work in number theory and combinatorics, including the development of Turán's theorem in extremal graph theory.
  • C. László Kalmár
    László Kalmár was a Hungarian mathematician known as a pioneer of theoretical computer science and mathematical logic in Hungary.
  • D. Lajos Takács
    Lajos Takács was a Hungarian-American mathematician renowned for his pioneering contributions to probability theory and queueing theory.
  • E. Béla Szőkefalvi-Nagy
    Béla Szőkefalvi-Nagy was a Hungarian mathematician renowned for his contributions to functional analysis and operator theory.
  • 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_69e11e4f2d0c819091aa3558ea2ee630 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f15a2af620819083338127e78137dc completed April 29, 2026, 1:08 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0bc9fd93e881908c7998b5a8118fad completed May 19, 2026, 2:25 a.m.
Created at: April 16, 2026, 8:47 p.m.