Triple
T22819389
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | XG-PON |
E565185
|
entity |
| Predicate | usesWavelengthDivisionMultiplexing |
P51279
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [XG-PON, usesWavelengthDivisionMultiplexing, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesWavelengthDivisionMultiplexing Context triple: [XG-PON, usesWavelengthDivisionMultiplexing, true]
-
A.
channelWavelength
Indicates the specific wavelength at which a given channel operates or is defined.
-
B.
supportsWavelengthRange
Indicates that an entity is capable of operating over, handling, or being compatible with a specified range of wavelengths.
-
C.
hasOpticalChannels
Indicates that an entity possesses one or more optical communication or signal-transmission channels.
-
D.
multiplexing
chosen
Indicates combining multiple signals or data streams into a single channel or medium for transmission or processing.
-
E.
usedWavelengthRange
Indicates the range of wavelengths that were employed or applied in performing a particular process, measurement, or operation.
- 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_69e2458426188190b58b8ab4844fe420 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17dcf39a88190bec26affc304236d |
completed | April 29, 2026, 3:41 a.m. |
| PD | Predicate disambiguation | batch_69eed2d117088190acbfe130d84f8627 |
completed | April 27, 2026, 3:06 a.m. |
Created at: April 17, 2026, 3:33 p.m.