Triple

T15803422
Position Surface form Disambiguated ID Type / Status
Subject E383150 entity
Predicate romanizationSystem P6517 FINISHED
Object Yale romanization E125415 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: Yale romanization | Statement: [趙, romanizationSystem, Yale romanization]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Yale romanization
Context triple: [趙, romanizationSystem, Yale romanization]
  • A. Yale romanization chosen
    Yale romanization is a widely used Latin-alphabet transcription system for Cantonese designed to represent pronunciation clearly for learners and linguistic study.
  • B. Hepburn romanization
    Hepburn romanization is a widely used system for transcribing Japanese sounds into the Latin alphabet, designed to be intuitive for English speakers.
  • C. Hakka Romanization System
    The Hakka Romanization System is a standardized method of writing the Hakka Chinese language using the Latin alphabet to represent its sounds and tones.
  • D. Kunrei-shiki romanization
    Kunrei-shiki romanization is a Japanese romanization system officially standardized in Japan that represents the language’s phonological structure more systematically than the widely used Hepburn system.
  • E. McCune–Reischauer
    McCune–Reischauer is a widely used system for romanizing the Korean language, designed to represent Korean pronunciation accurately using the Latin alphabet.
  • 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_69d86da2858c819090cc8481e7207b6e completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e0b524835c8190ae286b2562f07756 completed April 16, 2026, 10:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff90b4fe5881909471887219654d69 completed May 9, 2026, 7:53 p.m.
Created at: April 10, 2026, 4:48 a.m.