Triple
T18564364
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ornamental Dingbats |
E453723
|
entity |
| Predicate | codePointType |
P132520
|
FINISHED |
| Object | symbol |
—
|
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: symbol | Statement: [Ornamental Dingbats, codePointType, symbol]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: codePointType Context triple: [Ornamental Dingbats, codePointType, symbol]
-
A.
unicodeCodePoint
Indicates that a character or symbol is associated with a specific Unicode code point value in the Unicode standard.
-
B.
definesCodepoint
Indicates that one entity specifies or assigns the particular codepoint value used to represent another entity in an encoding system.
-
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.
UnicodeCodePointStandard
Indicates that a Unicode code point conforms to, or is defined within, a particular Unicode standard or version.
- F. None of above. chosen
Provenance (4 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_69d8d38974308190a9174430ef256b73 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e53afd8114819093b57d86f8213311 |
completed | April 19, 2026, 8:28 p.m. |
| PD | Predicate disambiguation | batch_69e478c16e0c8190b03966aa23c395a6 |
completed | April 19, 2026, 6:40 a.m. |
| PDg | Predicate description generation | batch_69e484121cd48190bf583b4c94636a30 |
completed | April 19, 2026, 7:28 a.m. |
Created at: April 10, 2026, 11:42 a.m.