Triple
T21718413
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Unicode 6.1 |
E536089
|
entity |
| Predicate | definesEncodingForm |
P66343
|
FINISHED |
| Object | UTF-8 |
—
|
NE NERFINISHED |
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: UTF-8 | Statement: [Unicode 6.1, definesEncodingForm, UTF-8]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: UTF-8 Context triple: [Unicode 6.1, definesEncodingForm, UTF-8]
-
A.
UTF-8
chosen
UTF-8 is a widely used variable-length character encoding standard for Unicode that efficiently represents text in most of the world's writing systems while maintaining backward compatibility with ASCII.
-
B.
UTF-16
UTF-16 is a variable-length character encoding for Unicode that represents most common characters in one 16-bit code unit and others, including supplementary characters, in pairs of 16-bit code units.
-
C.
Unicode
Unicode is a universal character encoding standard that assigns unique code points to virtually all written scripts, symbols, and emojis used in modern computing.
-
D.
UTF-7
UTF-7 is an obsolete, 7-bit Unicode text encoding designed primarily for safe transmission of Unicode data over email systems that were not fully 8-bit clean.
-
E.
UTF-32
UTF-32 is a fixed-length Unicode character encoding that represents each code point using 32 bits, providing simple indexing at the cost of higher memory usage.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c46c6dd88190a595375fa6ebd701 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69efd96cc58081908dda09819041b888 |
completed | April 27, 2026, 9:47 p.m. |
Created at: April 16, 2026, 6:47 p.m.