Triple

T18567491
Position Surface form Disambiguated ID Type / Status
Subject Punycode E453792 entity
Predicate basedOn P98 FINISHED
Object Bootstring algorithm NE NERFINISHED

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: Bootstring algorithm | Statement: [Punycode, basedOn, Bootstring algorithm]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bootstring algorithm
Context triple: [Punycode, basedOn, Bootstring algorithm]
  • A. Rabin–Karp algorithm
    The Rabin–Karp algorithm is a string-searching technique that uses hashing to efficiently find any one of a set of pattern strings in a text.
  • B. 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.
  • C. Boyer–Moore string-search algorithm
    The Boyer–Moore string-search algorithm is a highly efficient pattern-matching algorithm that scans text from right to left and uses precomputed shift rules to skip sections of the text, making it one of the fastest practical algorithms for substring search.
  • D. Aho–Corasick algorithm
    The Aho–Corasick algorithm is a classic string-searching algorithm that efficiently finds all occurrences of multiple patterns in a text using a trie-based finite-state machine.
  • E. 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.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Bootstring algorithm
Target entity description: The Bootstring algorithm is a general-purpose method for encoding strings of basic characters into compact ASCII representations, forming the basis for systems like Punycode used in internationalized domain names.
  • A. Rabin–Karp algorithm
    The Rabin–Karp algorithm is a string-searching technique that uses hashing to efficiently find any one of a set of pattern strings in a text.
  • B. 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.
  • C. Boyer–Moore string-search algorithm
    The Boyer–Moore string-search algorithm is a highly efficient pattern-matching algorithm that scans text from right to left and uses precomputed shift rules to skip sections of the text, making it one of the fastest practical algorithms for substring search.
  • D. Aho–Corasick algorithm
    The Aho–Corasick algorithm is a classic string-searching algorithm that efficiently finds all occurrences of multiple patterns in a text using a trie-based finite-state machine.
  • E. 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.
  • F. None of above. chosen

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_69d8d38974308190a9174430ef256b73 completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e53affc3e08190b4d16b5ccb0bddbc completed April 19, 2026, 8:28 p.m.
Created at: April 10, 2026, 11:43 a.m.