Triple

T2088720
Position Surface form Disambiguated ID Type / Status
Subject Moore's law E32615 entity
Predicate relatedConcept P37 FINISHED
Object Wirth's law E168966 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: Wirth's law | Statement: [Moore's law, relatedConcept, Wirth's law]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wirth's law
Context triple: [Moore's law, relatedConcept, Wirth's law]
  • A. Wirth’s law chosen
    Wirth’s law is the observation that software tends to become slower more quickly than hardware becomes faster, often negating the benefits of improved computing performance.
  • B. Koomey's law
    Koomey's law is an empirical observation that the energy efficiency of computing—measured as computations per unit of energy—has historically doubled roughly every 1.5 years.
  • C. Linus’s Law
    Linus’s Law is the open-source software development principle that “given enough eyeballs, all bugs are shallow,” emphasizing the power of many reviewers to quickly find and fix defects.
  • D. Kluge's law
    Kluge's law is a proposed sound law in Proto-Germanic historical linguistics that explains the development of certain geminate consonants from earlier consonant clusters.
  • E. Moore's law
    Moore's law is an observation and prediction that the number of transistors on an integrated circuit—and thus computing power—tends to roughly double at regular intervals, driving exponential growth in digital technology.
  • 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_69a885eba0708190999696a45cbec816 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69abba712388819091d68a4bb99f6b17 completed March 7, 2026, 5:41 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae3058a7b48190879edde4d97ca102 completed March 9, 2026, 2:28 a.m.
Created at: March 4, 2026, 7:43 p.m.