Triple

T2268072
Position Surface form Disambiguated ID Type / Status
Subject Andrew Viterbi E50192 entity
Predicate hasAlgorithmNamedAfter P37563 FINISHED
Object Viterbi algorithm E252270 NE FINISHED

How this triple was built (3 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: Viterbi algorithm | Statement: [Andrew Viterbi, hasAlgorithmNamedAfter, Viterbi algorithm]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Viterbi algorithm
Context triple: [Andrew Viterbi, hasAlgorithmNamedAfter, Viterbi algorithm]
  • A. Viterbi algorithm chosen
    The Viterbi algorithm is a dynamic programming method used to find the most likely sequence of hidden states in probabilistic models such as Hidden Markov Models, widely applied in fields like digital communications, speech recognition, and bioinformatics.
  • B. 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.
  • C. Thompson's algorithm
    Thompson's algorithm is a classic computer science method for converting regular expressions into nondeterministic finite automata (NFAs), widely used in pattern matching and lexical analysis.
  • D. Marzullo's algorithm
    Marzullo's algorithm is a method for selecting the most likely correct time interval from multiple, possibly conflicting time sources, commonly used in clock synchronization systems.
  • E. Knuth–Morris–Pratt algorithm
    The Knuth–Morris–Pratt algorithm is a classic linear-time string-searching algorithm that efficiently finds occurrences of a pattern within a text by precomputing a prefix function to avoid redundant comparisons.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasAlgorithmNamedAfter
Context triple: [Andrew Viterbi, hasAlgorithmNamedAfter, Viterbi algorithm]
  • A. hasTheoremNamedAfter
    Indicates that a theorem is named in honor of or after a particular person or entity.
  • B. hasAwardNamedAfter
    Indicates that an entity has an award that is named in honor of another entity.
  • C. hasLawNamedAfter
    Indicates that a law or piece of legislation is named in honor of, or directly after, a particular entity.
  • D. hasSymbolNamedAfter
    Indicates that one entity has a symbol whose name is derived from or dedicated to another entity.
  • E. hasPlaceNamedAfter
    Indicates that one place is named in honor of or derived from the name of another place.
  • F. None of above. chosen

Provenance (5 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_69a88b01e0048190ba96431b5f990ba9 completed March 4, 2026, 7:41 p.m.
NER Named-entity recognition batch_69abc2ea65288190bc8644a07a11dfa9 completed March 7, 2026, 6:17 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae894a20e0819097f08959f7a062ef completed March 9, 2026, 8:48 a.m.
PD Predicate disambiguation batch_69abbdb592588190ac1ef5e8c54575b1 completed March 7, 2026, 5:55 a.m.
PDg Predicate description generation batch_69abc2e97eb0819084acb26cfa4e3946 completed March 7, 2026, 6:17 a.m.
Created at: March 4, 2026, 7:48 p.m.