Triple
T30477013
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CJK Compatibility Ideographs |
E775469
|
entity |
| Predicate | containsNumberOfCodePoints |
P67238
|
FINISHED |
| Object | 256 |
—
|
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: 256 | Statement: [CJK Compatibility Ideographs, containsNumberOfCodePoints, 256]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: containsNumberOfCodePoints Context triple: [CJK Compatibility Ideographs, containsNumberOfCodePoints, 256]
-
A.
blockNumberOfCodePoints
chosen
Indicates the number of code points contained within a given block.
-
B.
usesCodePoints
Indicates that one entity represents, encodes, or operates using the specific set of Unicode code points defined by another entity.
-
C.
codePointCount
Indicates the number of Unicode code points contained within a specified range of a character sequence.
-
D.
hasUnicodeCodePoint
Indicates that a character or symbol is associated with a specific numeric Unicode code point value.
-
E.
maximumCodePoints
Indicates the maximum number of Unicode code points that are allowed or supported in a given context or value.
- 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_69f22497341481909c21ba329fadaa6b |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69ff109695008190a22b47ef8be2e3f9 |
completed | May 9, 2026, 10:46 a.m. |
| PD | Predicate disambiguation | batch_69ff0f243ea88190970d2c520b55c816 |
completed | May 9, 2026, 10:40 a.m. |
Created at: April 29, 2026, 8:12 p.m.