Triple
T21985024
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hangul Syllables |
E542934
|
entity |
| Predicate | firstCodePointName |
P146158
|
FINISHED |
| Object | HANGUL SYLLABLE GA |
—
|
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: HANGUL SYLLABLE GA | Statement: [Hangul Syllables, firstCodePointName, HANGUL SYLLABLE GA]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstCodePointName Context triple: [Hangul Syllables, firstCodePointName, HANGUL SYLLABLE GA]
-
A.
unicodeCodePoint
Indicates that a character or symbol is associated with a specific Unicode code point value in the Unicode standard.
-
B.
definesCodepoint
Indicates that one entity specifies or assigns the particular codepoint value used to represent another entity in an encoding system.
-
C.
UnicodeCodePointStandard
Indicates that a Unicode code point conforms to, or is defined within, a particular Unicode standard or version.
-
D.
hasUnicodeCodePoint
Indicates that a character or symbol is associated with a specific numeric Unicode code point value.
-
E.
codePointType
Indicates the classification or category assigned to a specific Unicode code point (such as letter, digit, punctuation, etc.).
- 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_69e0c48136b081908831fa907cc02e18 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f12708cdcc81909511d9f81bd8f20e |
completed | April 28, 2026, 9:30 p.m. |
| PD | Predicate disambiguation | batch_69e6f6154e408190acc5b2c278acaff4 |
completed | April 21, 2026, 3:59 a.m. |
| PDg | Predicate description generation | batch_69e6fad4a540819096cdd5ea08527220 |
completed | April 21, 2026, 4:19 a.m. |
Created at: April 16, 2026, 8:04 p.m.