Triple
T10159023
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EFnet |
E233840
|
entity |
| Predicate | approximateChannelCountPeak |
P82796
|
FINISHED |
| Object | several tens of thousands of channels at historical peak |
—
|
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: several tens of thousands of channels at historical peak | Statement: [EFnet, approximateChannelCountPeak, several tens of thousands of channels at historical peak]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateChannelCountPeak Context triple: [EFnet, approximateChannelCountPeak, several tens of thousands of channels at historical peak]
-
A.
hasChannelCount
chosen
Indicates that an entity is associated with a specific number of channels.
-
B.
maxMemoryChannels
Indicates the maximum number of memory channels that an entity can support or utilize.
-
C.
audioChannels
Indicates the number or configuration of distinct audio signal paths (such as mono, stereo, or surround) used in a recording, transmission, or playback.
-
D.
hasNumberOfADPCMAChannels
Indicates the quantity of ADPCM (Adaptive Differential Pulse-Code Modulation) channels associated with or supported by an entity.
-
E.
maximumChannelWidth
Indicates the greatest allowable or observed width of a channel in the given context.
- 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_69ca848e80748190b91d1e04d35512c7 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cdec56d944819081bc6ea36c905ba2 |
completed | April 2, 2026, 4:11 a.m. |
| PD | Predicate disambiguation | batch_69cd4ba795808190acc9124c98c6e40f |
completed | April 1, 2026, 4:45 p.m. |
Created at: March 30, 2026, 9:09 p.m.