Triple
T1079927
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ASCII |
E23922
|
entity |
| Predicate | characterEncodingType |
P20982
|
FINISHED |
| Object | single-byte encoding |
—
|
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: single-byte encoding | Statement: [ASCII, characterEncodingType, single-byte encoding]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterEncodingType Context triple: [ASCII, characterEncodingType, single-byte encoding]
-
A.
characterSetType
Indicates the type or category of character set associated with or used by an entity.
-
B.
codingSystemType
chosen
Indicates the classification or category of coding system used to encode or represent information in a given context.
-
C.
usesCharacterSet
Indicates that one entity employs or relies on a specific character set defined by another entity for encoding or representing text.
-
D.
colorEncoding
Indicates how the color information of an entity is represented, formatted, or encoded.
-
E.
colorEncodingMethod
Indicates the method or scheme used to represent or encode color information.
- 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_69a493f1ddf48190a99d54b00e99f8ce |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b94509d08190964509ea4a2d7912 |
completed | March 1, 2026, 10:10 p.m. |
| PD | Predicate disambiguation | batch_69a4b73d9f08819093668104f129840e |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:42 p.m.