Triple
T3383766
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Unicode 15.0 |
E71247
|
entity |
| Predicate | addsEmojiSequencesCount |
P47139
|
FINISHED |
| Object | 11 |
—
|
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: 11 | Statement: [Unicode 15.0, addsEmojiSequencesCount, 11]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: addsEmojiSequencesCount Context triple: [Unicode 15.0, addsEmojiSequencesCount, 11]
-
A.
maximumCodePoints
Indicates the maximum number of Unicode code points that are allowed or supported in a given context or value.
-
B.
hasCombiningMarks
Indicates that an entity (such as a character or string) includes one or more combining marks attached to a base element.
-
C.
graphicCharactersCount
Indicates the number of printable (non-control) characters present in a given text or string.
-
D.
evaCount
Indicates a relationship where a specific count or number is associated with an evaluation-related event, action, or occurrence.
-
E.
hasStrokeCountApprox
Indicates an approximate number of strokes associated with writing or drawing the related entity.
- 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.