Triple

T15791414
Position Surface form Disambiguated ID Type / Status
Subject Manual of Political Economy E382869 entity
Predicate hasConcept P531 FINISHED
Object Pareto law E382868 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: Pareto law | Statement: [Manual of Political Economy, hasConcept, Pareto law]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pareto law
Context triple: [Manual of Political Economy, hasConcept, Pareto law]
  • A. Pareto distribution chosen
    The Pareto distribution is a power-law probability distribution often used to model phenomena with heavy tails and strong inequality, such as wealth or city sizes.
  • B. Zipf's law
    Zipf's law is an empirical statistical principle observing that in many datasets, such as word frequencies in natural language, the frequency of an item is inversely proportional to its rank in a frequency table.
  • C. Pareto principle
    The Pareto principle is an economic and management concept stating that roughly 80% of effects come from 20% of causes, often used to prioritize efforts and resources.
  • D. Lusser's law
    Lusser's law is a reliability engineering principle that states the overall reliability of a system is the product of the reliabilities of its individual components, highlighting how system reliability decreases as more components are added in series.
  • E. Szemerényi's law
    Szemerényi's law is a sound law in Proto-Indo-European linguistics that explains the loss of certain final consonants with compensatory lengthening of the preceding vowel.
  • 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_69d86da16e188190b89af699f1ed0bfe completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e0b4d819c881908bc43a6124a1bb2e completed April 16, 2026, 10:07 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff90a87e3c8190a1c5b13cbfdff54a completed May 9, 2026, 7:53 p.m.
Created at: April 10, 2026, 4:48 a.m.