Triple

T21984969
Position Surface form Disambiguated ID Type / Status
Subject Eonmun E542933 entity
Predicate contrastWith P278 FINISHED
Object Hanja NE NERFINISHED

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: Hanja | Statement: [Eonmun, contrastWith, Hanja]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hanja
Context triple: [Eonmun, contrastWith, Hanja]
  • A. Hanja chosen
    Hanja is the set of traditional Chinese characters historically used to write Korean, especially for proper names, academic terms, and classical texts.
  • B. Hangul
    Hangul is the native alphabetic writing system of the Korean language, renowned for its scientific design and ease of learning.
  • C. Yi script
    Yi script is a traditional logographic and syllabic writing system used to represent the Yi languages of southwestern China.
  • D. Hangul Jamo
    Hangul Jamo is a Unicode block that encodes the individual consonant and vowel letters used to write the Korean Hangul script.
  • E. Hangul Syllables
    Hangul Syllables is the Unicode block that encodes the precomposed modern Korean syllabic characters used for writing Hangul.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0c48136b081908831fa907cc02e18 completed April 16, 2026, 11:14 a.m.
NER Named-entity recognition batch_69f12708cdcc81909511d9f81bd8f20e completed April 28, 2026, 9:30 p.m.
Created at: April 16, 2026, 8:04 p.m.