Triple
T18564520
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UTF-16 |
E453728
|
entity |
| Predicate | surrogatePairSize |
P132527
|
FINISHED |
| Object | 2 code units |
—
|
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: 2 code units | Statement: [UTF-16, surrogatePairSize, 2 code units]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: surrogatePairSize Context triple: [UTF-16, surrogatePairSize, 2 code units]
-
A.
maximumCodePoints
Indicates the maximum number of Unicode code points that are allowed or supported in a given context or value.
-
B.
blockNumberOfCodePoints
Indicates the number of code points contained within a given block.
-
C.
codePointCount
Indicates the number of Unicode code points contained within a specified range of a character sequence.
-
D.
maxBytesPerCodePoint
Indicates the maximum number of bytes used to encode a single code point in a given character encoding or data representation.
-
E.
minBytesPerCodePoint
Indicates the minimum number of bytes required to represent a single code point in the given encoding or data representation.
- 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.