Triple
T22110141
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SIP |
E546390
|
entity |
| Predicate | isInBasicMultilingualPlane |
P132528
|
FINISHED |
| Object | false |
—
|
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: false | Statement: [SIP, isInBasicMultilingualPlane, false]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isInBasicMultilingualPlane Context triple: [SIP, isInBasicMultilingualPlane, false]
-
A.
usesBasicMultilingualPlane
chosen
Indicates that the subject operates within or is limited to the Unicode Basic Multilingual Plane (the first 65,536 code points), rather than using supplementary planes.
-
B.
hasNumberOfBasicCharacters
Indicates the quantity of basic (non-accented or fundamental) characters associated with an entity.
-
C.
hasBlockUnicode
Indicates that one entity possesses or is associated with a specific Unicode block related to another entity.
-
D.
UnicodePlane
Indicates that a Unicode code point belongs to a specific Unicode plane (a contiguous range of code points grouped by plane number).
-
E.
basicMultilingualPlaneRange
Indicates that the referenced value or code point range lies within the Basic Multilingual Plane (BMP) of the Unicode character set.
- 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_69e11e378dc08190896d6a51597afd5a |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f12948c2ec819083340787b2062649 |
completed | April 28, 2026, 9:40 p.m. |
| PD | Predicate disambiguation | batch_69e71b2ed7348190b6fa2e52f54393fb |
completed | April 21, 2026, 6:37 a.m. |
Created at: April 16, 2026, 8:30 p.m.