Triple
T3383762
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Unicode 15.0 |
E71247
|
entity |
| Predicate | addsCharactersCount |
P47136
|
FINISHED |
| Object | 4488 |
—
|
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: 4488 | Statement: [Unicode 15.0, addsCharactersCount, 4488]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: addsCharactersCount Context triple: [Unicode 15.0, addsCharactersCount, 4488]
-
A.
graphicCharactersCount
Indicates the number of printable (non-control) characters present in a given text or string.
-
B.
numberOfCharacters
Indicates the total count of individual characters present in a given text, string, or entity’s representation.
-
C.
usesCharactersAs
Indicates that one entity employs or incorporates specific characters (such as letters, symbols, or glyphs) from another entity for its representation or functioning.
-
D.
numberOfCommonUseCharacters
Indicates the count of characters that are shared in common between two entities’ representations or strings.
-
E.
hasLetterCount
Indicates that an entity is associated with a specific number representing how many letters it contains.
- 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_69ad85a8fd9c819095ecedf838d2bf1b |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb5ec85d08190b28110157c39435f |
completed | March 8, 2026, 5:46 p.m. |
| PD | Predicate disambiguation | batch_69ada434bae48190a77ea37f9274ad8f |
completed | March 8, 2026, 4:30 p.m. |
| PDg | Predicate description generation | batch_69ada527ff308190813a7ffdcdec4322 |
completed | March 8, 2026, 4:34 p.m. |
Created at: March 8, 2026, 3:14 p.m.