Triple

T28188187
Position Surface form Disambiguated ID Type / Status
Subject Japanese honors system E716232 entity
Predicate reformContent P4888 FINISHED
Object introduction of gender-neutral designs 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: introduction of gender-neutral designs | Statement: [Japanese honors system, reformContent, introduction of gender-neutral designs]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: reformContent
Context triple: [Japanese honors system, reformContent, introduction of gender-neutral designs]
  • A. reform chosen
    Indicates bringing about significant changes to an existing system, practice, or entity in order to improve or correct it.
  • B. requestedReform
    Indicates that one entity has asked another entity to implement or consider a specific reform or change to an existing system, policy, or practice.
  • C. typeOfReformBody
    Indicates that one entity is a reform body that is classified as a specific type or category of reform body represented by the other entity.
  • D. reformsBy
    Indicates that one entity initiates, implements, or is responsible for changes or improvements (reforms) affecting another entity.
  • E. canReform
    Indicates that an entity has the ability or potential to change, improve, or be corrected from a previous state, behavior, or condition.
  • 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_69efd6b612f48190a72012b520afbd10 completed April 27, 2026, 9:35 p.m.
NER Named-entity recognition batch_69f643ed0b7481908cf25f3afec0a61d completed May 2, 2026, 6:35 p.m.
PD Predicate disambiguation batch_69f641def1e88190a05bf865ced78b23 completed May 2, 2026, 6:26 p.m.
Created at: April 27, 2026, 10:24 p.m.