Triple
T33027319
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EBCDIC |
E845075
|
entity |
| Predicate | hasPrintableCharacterRange |
P23705
|
FINISHED |
| Object | 0x40–0xFF (variant-dependent) |
—
|
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: 0x40–0xFF (variant-dependent) | Statement: [EBCDIC, hasPrintableCharacterRange, 0x40–0xFF (variant-dependent)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPrintableCharacterRange Context triple: [EBCDIC, hasPrintableCharacterRange, 0x40–0xFF (variant-dependent)]
-
A.
printableCharacterRange
chosen
Indicates the range of characters that are considered printable within a given character set or encoding.
-
B.
hasControlCharacterRange
Indicates that there exists a specified range of control characters associated with or applicable to an entity.
-
C.
hasTextualCharacter
Indicates that something possesses or exhibits the qualities of written or printed text, such as letters, symbols, or characters.
-
D.
hasNumberOfBasicCharacters
Indicates the quantity of basic (non-accented or fundamental) characters associated with an entity.
-
E.
hasKeyboardRange
Indicates that one entity possesses or supports a specified span of keys or notes across a keyboard-based input or instrument.
- 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_69f34950749c8190ae05cd27adb16d58 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a01487b73488190954eb5143e6f246e |
completed | May 11, 2026, 3:09 a.m. |
| PD | Predicate disambiguation | batch_6a0145210ae481908da59b02efdbc397 |
completed | May 11, 2026, 2:55 a.m. |
Created at: May 1, 2026, 1:23 a.m.