Triple
T30447210
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yi Radicals |
E774610
|
entity |
| Predicate | hasCodePointProperty |
P26444
|
FINISHED |
| Object | General_Category=Lo for most characters |
—
|
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: General_Category=Lo for most characters | Statement: [Yi Radicals, hasCodePointProperty, General_Category=Lo for most characters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCodePointProperty Context triple: [Yi Radicals, hasCodePointProperty, General_Category=Lo for most characters]
-
A.
hasUnicodeCodePoint
Indicates that a character or symbol is associated with a specific numeric Unicode code point value.
-
B.
hasUnicodeProperty
chosen
Indicates that an entity possesses a specific Unicode character property or set of properties (such as category, script, or other Unicode-defined attributes).
-
C.
definesCodepoint
Indicates that one entity specifies or assigns the particular codepoint value used to represent another entity in an encoding system.
-
D.
usesCodePoints
Indicates that one entity represents, encodes, or operates using the specific set of Unicode code points defined by another entity.
-
E.
codePointType
Indicates the classification or category assigned to a specific Unicode code point (such as letter, digit, punctuation, etc.).
- 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_69f22493ef9c8190ae8c2afcb7f994c8 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_6a0143a4ab3c8190a240b0facfe130a9 |
completed | May 11, 2026, 2:49 a.m. |
| PD | Predicate disambiguation | batch_6a01426b2d4481908654bfa4a02c457d |
completed | May 11, 2026, 2:43 a.m. |
Created at: April 29, 2026, 8:09 p.m.