Triple
T16496318
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AIRS |
E400692
|
entity |
| Predicate | numberOfInfraredChannels |
P123770
|
FINISHED |
| Object | 2378 |
—
|
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: 2378 | Statement: [AIRS, numberOfInfraredChannels, 2378]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfInfraredChannels Context triple: [AIRS, numberOfInfraredChannels, 2378]
-
A.
hasOpticalChannels
Indicates that an entity possesses one or more optical communication or signal-transmission channels.
-
B.
numberOfAdditionalChannels
Indicates the quantity of extra channels added beyond a base or default set in a given context.
-
C.
supportsInfraredConnectivity
Indicates that one entity provides or enables infrared-based communication or data transfer capabilities for another entity.
-
D.
imageChannels
Indicates the number and types of color or data channels present in an image (e.g., RGB, RGBA, grayscale).
-
E.
inputColorChannels
Indicates the specific color channels that are provided as input to a process, function, or model.
- 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_69d88381f6148190819958a038be990e |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e32e332c1c8190888a042d0192233a |
completed | April 18, 2026, 7:09 a.m. |
| PD | Predicate disambiguation | batch_69e296902d6c8190884ddb612b8c5b36 |
completed | April 17, 2026, 8:22 p.m. |
| PDg | Predicate description generation | batch_69e2d7f97e548190a474691a152bd8e8 |
completed | April 18, 2026, 1:01 a.m. |
Created at: April 10, 2026, 5:14 a.m.