Triple

T10236315
Position Surface form Disambiguated ID Type / Status
Subject Buchberger algorithm E243471 entity
Predicate hasVariant P455 FINISHED
Object F4 algorithm E838596 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: F4 algorithm | Statement: [Buchberger algorithm, hasVariant, F4 algorithm]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: F4 algorithm
Context triple: [Buchberger algorithm, hasVariant, F4 algorithm]
  • A. F4 algorithm chosen
    The F4 algorithm is an efficient method for computing Gröbner bases using structured linear algebra techniques to speed up polynomial ideal calculations.
  • B. F5 algorithm
    The F5 algorithm is an efficient method in computational algebra for computing Gröbner bases by using signature-based criteria to avoid redundant polynomial reductions.
  • C. Benettin algorithm
    The Benettin algorithm is a numerical method used in dynamical systems theory to estimate Lyapunov exponents, which quantify the rate of separation of nearby trajectories and indicate chaos.
  • D. Forney algorithm
    The Forney algorithm is a key error-location and error-value computation method used in decoding Reed–Solomon and other BCH error-correcting codes in digital communication systems.
  • E. Berlekamp–Massey algorithm
    The Berlekamp–Massey algorithm is a key algorithm in coding theory and cryptography used to efficiently determine the shortest linear feedback shift register that generates a given binary sequence.
  • 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_69d381b0f97c819085c9b45799a5fb7c completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4d219ab04819094a17c96bf1d65ae completed April 7, 2026, 9:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69d6f762732481909246dcb768074643 completed April 9, 2026, 12:48 a.m.
Created at: April 6, 2026, 11:22 a.m.