Triple
T1468068
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CMARC |
E27069
|
entity |
| Predicate | characterEncodingSupport |
P7661
|
FINISHED |
| Object | Chinese 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: Chinese characters | Statement: [CMARC, characterEncodingSupport, Chinese characters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterEncodingSupport Context triple: [CMARC, characterEncodingSupport, Chinese characters]
-
A.
usesCharacterSet
chosen
Indicates that one entity employs or relies on a specific character set defined by another entity for encoding or representing text.
-
B.
hasUnicode
Indicates that an entity is associated with, represented by, or encoded using a specific Unicode character or sequence.
-
C.
encodedInUnicodeSince
Indicates that a given character or symbol has been included and assigned a code point in the Unicode standard starting from a specific version or time.
-
D.
hasUnicodeStatus
Indicates that a given entity has a particular Unicode-related classification or status (such as assigned, reserved, deprecated, or noncharacter) within the Unicode standard.
-
E.
codingSystemType
Indicates the classification or category of coding system used to encode or represent information in a given context.
- 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_69a496d25d6881909dbd84f86d763992 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c5d70a948190b50a6c1b36abc740 |
completed | March 1, 2026, 11:03 p.m. |
| PD | Predicate disambiguation | batch_69a4c48121e48190946c23c583e5fb64 |
completed | March 1, 2026, 10:58 p.m. |
Created at: March 1, 2026, 8:01 p.m.