Triple
T31664914
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Munhwaŏ |
E808098
|
entity |
| Predicate | hasStandardVocabularyPolicy |
P198141
|
FINISHED |
| Object | preference for native Korean words |
—
|
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: preference for native Korean words | Statement: [Munhwaŏ, hasStandardVocabularyPolicy, preference for native Korean words]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStandardVocabularyPolicy Context triple: [Munhwaŏ, hasStandardVocabularyPolicy, preference for native Korean words]
-
A.
hasVocabularyFrom
Indicates that one entity’s vocabulary, terminology, or set of terms is derived from, based on, or taken from another entity.
-
B.
hasVocabularySize
Indicates the size or number of vocabulary items possessed or used by an entity.
-
C.
hasKnownVocabulary
Indicates that an entity possesses a defined, identifiable set of terms or words that it can recognize or use.
-
D.
hasDistinctVocabulary
Indicates that one entity’s vocabulary is different or distinguishable from that of another entity.
-
E.
hasStandardizedGrammar
Indicates that a language or notation follows an officially defined and consistently applied set of grammatical rules.
- F. None of above. chosen
Provenance (4 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_69f348dbeef4819080b446a7feb6340b |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69fecd0a732c819097bdd3eb69b6158c |
completed | May 9, 2026, 5:58 a.m. |
| PD | Predicate disambiguation | batch_69fecc0318d481908b5b20598a76a9fe |
completed | May 9, 2026, 5:54 a.m. |
| PDg | Predicate description generation | batch_69fecd091abc8190872974de731a80fa |
completed | May 9, 2026, 5:58 a.m. |
Created at: April 30, 2026, 10:58 p.m.