Triple
T34383112
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeong |
E882487
|
entity |
| Predicate | isHomophonousWith |
P119509
|
FINISHED |
| Object | multiple hanja characters pronounced 정 |
—
|
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: multiple hanja characters pronounced 정 | Statement: [Jeong, isHomophonousWith, multiple hanja characters pronounced 정]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isHomophonousWith Context triple: [Jeong, isHomophonousWith, multiple hanja characters pronounced 정]
-
A.
hasHomophones
chosen
Indicates that two or more linguistic expressions share the same pronunciation but differ in meaning, spelling, or both.
-
B.
isHomographOf
Indicates that two words share the same written form but have different meanings, and possibly different pronunciations or origins.
-
C.
hasPhonologicalSimilarityTo
Indicates that two linguistic elements share similar sound patterns or phonological features.
-
D.
heteronymOf
Indicates that two words share the same spelling but differ in pronunciation and meaning.
-
E.
hasDistinctLetterForSound
Indicates that a particular sound is represented by its own unique letter, distinct from other sounds in the writing system.
- 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_69f349c0219881909393bbbc1edc8161 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f71c35327c8190884f1bfe12bd2cd7 |
completed | May 3, 2026, 9:58 a.m. |
| PD | Predicate disambiguation | batch_69f71822d0e88190ac9731c7ae5a4def |
completed | May 3, 2026, 9:40 a.m. |
Created at: May 1, 2026, 1:59 a.m.