Triple

T35798040
Position Surface form Disambiguated ID Type / Status
Subject Cheung Chau Bun Festival E1034889 entity
Predicate bunSymbolism P107935 FINISHED
Object peace and good fortune 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: peace and good fortune | Statement: [Cheung Chau Bun Festival, bunSymbolism, peace and good fortune]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: bunSymbolism
Context triple: [Cheung Chau Bun Festival, bunSymbolism, peace and good fortune]
  • A. symbolismIn chosen
    Indicates that one entity functions as a symbol or representation within the context, meaning, or interpretive framework of another entity.
  • B. symbolismFocus
    Indicates that the primary emphasis of a work, element, or representation is on its symbolic meaning rather than its literal or functional aspects.
  • C. languageOfSymbolism
    Indicates that one entity is the language in which the symbolic meaning or symbolism of another entity is expressed or encoded.
  • D. fieldSymbolism
    Indicates the symbolic meaning or thematic associations that a field (as a setting, area, or domain) conveys within a given context.
  • E. explainsSymbolismOf
    Indicates that one entity provides an interpretation or clarification of the symbolic meaning contained in another entity.
  • 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_69f76e169bd081909f16cd8c9ee7870c completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f7a25600d48190a3b8197343038068 completed May 3, 2026, 7:30 p.m.
PD Predicate disambiguation batch_69f7a070e23881909a233370acb57384 completed May 3, 2026, 7:22 p.m.
Created at: May 3, 2026, 4:06 p.m.