Triple

T7755164
Position Surface form Disambiguated ID Type / Status
Subject An ounce of prevention is worth a pound of cure E175874 entity
Predicate hasRhetoricalFunction P834 FINISHED
Object persuasion LITERAL 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: persuasion | Statement: [An ounce of prevention is worth a pound of cure, hasRhetoricalFunction, persuasion]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasRhetoricalFunction
Context triple: [An ounce of prevention is worth a pound of cure, hasRhetoricalFunction, persuasion]
  • A. hasFictionalFunction
    Indicates that an entity serves a role, purpose, or function within a fictional context or narrative.
  • B. containsRhetoricalQuestions
    Indicates that the text includes questions posed for effect or persuasion rather than to elicit actual answers.
  • C. rhetoricalDevice chosen
    Indicates that one entity is used as a rhetorical device in relation to another, such as a figure of speech, stylistic technique, or persuasive strategy within a discourse.
  • D. rhetoricalStyle
    Indicates the characteristic manner or technique of expression used in communication, such as tone, structure, and persuasive strategies.
  • E. textualFunction
    Indicates a functional or structural role that a text segment serves within a larger document or discourse (e.g., title, caption, summary, instruction).
  • F. None of above.

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_69c6996180088190832e38e8d83ff54a completed March 27, 2026, 2:51 p.m.
NER Named-entity recognition batch_69c705257ca08190a78c592a1e616da8 completed March 27, 2026, 10:31 p.m.
PD Predicate disambiguation batch_69c7016df2b08190b2330a2010691431 completed March 27, 2026, 10:15 p.m.
Created at: March 27, 2026, 4:08 p.m.