Triple
T5634099
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Default Unicode Collation Element Table |
E147902
|
entity |
| Predicate | characterCoverage |
P64709
|
FINISHED |
| Object | all Unicode planes |
—
|
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: all Unicode planes | Statement: [Default Unicode Collation Element Table, characterCoverage, all Unicode planes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterCoverage Context triple: [Default Unicode Collation Element Table, characterCoverage, all Unicode planes]
-
A.
characterSetName
Indicates the name assigned to a particular character set used for encoding or representing characters.
-
B.
character2
Indicates that a second character entity is involved in the relationship or context defined by the predicate.
-
C.
textCharacter
Indicates that one entity is a character (such as a letter, digit, or symbol) within a piece of text associated with another entity.
-
D.
regionCharacter
Indicates a characteristic, feature, or quality that typifies or defines a particular region.
-
E.
characterAddressed
Indicates that one character directs speech, communication, or attention specifically toward another character.
- 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_69c00907bc8881909ed760d3ed73ef35 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c0226118548190877793dadf6cacba |
completed | March 22, 2026, 5:09 p.m. |
| PD | Predicate disambiguation | batch_69c01b1f12ec8190b4b9d9ee31cabe19 |
completed | March 22, 2026, 4:38 p.m. |
| PDg | Predicate description generation | batch_69c01f0684908190ae2d14f0bd2ab892 |
completed | March 22, 2026, 4:55 p.m. |
Created at: March 22, 2026, 3:41 p.m.