Triple
T22604579
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frequency Division Multiple Access |
E566520
|
entity |
| Predicate | channelAssignmentType |
P147831
|
FINISHED |
| Object | orthogonal frequency channels |
—
|
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: orthogonal frequency channels | Statement: [Frequency Division Multiple Access, channelAssignmentType, orthogonal frequency channels]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: channelAssignmentType Context triple: [Frequency Division Multiple Access, channelAssignmentType, orthogonal frequency channels]
-
A.
channelAllocation
chosen
Indicates how communication or data channels are assigned or distributed among entities or resources.
-
B.
channelLining
Indicates the presence or application of a protective or functional lining along the interior surface of a channel or conduit.
-
C.
channelSpecialization
Indicates that one channel is a more specialized or focused version of another, typically refining or narrowing the scope of the broader channel.
-
D.
channelCategory
Indicates that a channel belongs to or is classified under a particular category.
-
E.
channelNumber
Indicates the specific numeric identifier assigned to a channel within a system or medium.
- 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_69e245884860819081046ce07d5872c4 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f1626fad6881909895cc8c0af62f0d |
completed | April 29, 2026, 1:44 a.m. |
| PD | Predicate disambiguation | batch_69ee627be4248190889a88764624e174 |
completed | April 26, 2026, 7:07 p.m. |
Created at: April 17, 2026, 2:52 p.m.