Triple

T5690941
Position Surface form Disambiguated ID Type / Status
Subject Hangul Day E125425 entity
Predicate relatedTo P37 FINISHED
Object Hangul E25453 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: Hangul | Statement: [Hangul Day, relatedTo, Hangul]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hangul
Context triple: [Hangul Day, relatedTo, Hangul]
  • A. Hangul chosen
    Hangul is the native alphabetic writing system of the Korean language, renowned for its scientific design and ease of learning.
  • B. Hanja
    Hanja is the set of traditional Chinese characters historically used to write Korean, especially for proper names, academic terms, and classical texts.
  • C. Korean
    Korean is an East Asian language spoken primarily in both North and South Korea, known for its unique Hangul writing system and distinct linguistic structure.
  • D. Hangul Syllables
    Hangul Syllables is the Unicode block that encodes the precomposed modern Korean syllabic characters used for writing Hangul.
  • E. Baeggu language
    The Baeggu language is an Oceanic language spoken by the Baeggu people in the Solomon Islands, belonging to the Southeast Solomonic branch of the Austronesian language family.
  • 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_69c0082bb19c8190823a4facd3cba79b completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c023e500ec8190bfda4f6a818aa5dc completed March 22, 2026, 5:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69c097e2c0648190abedcd9ea463eb84 completed March 23, 2026, 1:31 a.m.
Created at: March 22, 2026, 3:44 p.m.