Triple
T30534196
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Unicode 2.1 |
E777094
|
entity |
| Predicate | hasCharacterRepertoireSize |
P58060
|
FINISHED |
| Object | approximately 38,885 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: approximately 38,885 characters | Statement: [Unicode 2.1, hasCharacterRepertoireSize, approximately 38,885 characters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCharacterRepertoireSize Context triple: [Unicode 2.1, hasCharacterRepertoireSize, approximately 38,885 characters]
-
A.
hasGlyphRepertoireSize
Indicates the number of distinct glyphs included in an entity’s glyph repertoire.
-
B.
hasCharacterSetSizeCategory
Indicates the relationship between something and the category that classifies the size of its character set.
-
C.
characterSetSize
chosen
Indicates the total number of distinct characters contained in or allowed by a given character set.
-
D.
hasNumberOfBasicCharacters
Indicates the quantity of basic (non-accented or fundamental) characters associated with an entity.
-
E.
hasFullSizedCharacters
Indicates that the subject includes characters rendered at full, standard size rather than reduced or miniature forms.
- 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_69f2249c11508190ae7e955755ccfb01 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69fd19f791f48190bbb6f6047f9ddc59 |
completed | May 7, 2026, 11:02 p.m. |
| PD | Predicate disambiguation | batch_69fd0df365948190bc9bfc7ffd46acd8 |
completed | May 7, 2026, 10:10 p.m. |
Created at: April 29, 2026, 8:18 p.m.