Triple
T22056870
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | THEMIS instrument |
E545042
|
entity |
| Predicate | spatialResolutionInfrared |
P142931
|
FINISHED |
| Object | about 100 meters per pixel |
—
|
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: about 100 meters per pixel | Statement: [THEMIS instrument, spatialResolutionInfrared, about 100 meters per pixel]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spatialResolutionInfrared Context triple: [THEMIS instrument, spatialResolutionInfrared, about 100 meters per pixel]
-
A.
hasSpatialResolution
Indicates that something is characterized by a specific level of spatial detail or granularity at which it can represent or distinguish features in space.
-
B.
sensorResolution
Indicates the level of detail or precision with which a sensor can measure or distinguish changes in the observed quantity or environment.
-
C.
imageResolutionThermal
chosen
Indicates the level of detail or clarity in a thermal image, typically expressed as the number of pixels or spatial resolution of the thermal sensor.
-
D.
numberOfInfraredChannels
Indicates the count of distinct infrared channels associated with or supported by an entity.
-
E.
spectralResolution
Indicates the fineness with which a system can distinguish or separate different wavelengths or frequencies within a spectrum.
- 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_69e11e3377c48190890c17407b9527d6 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f128588e0081909056ac8251afe935 |
completed | April 28, 2026, 9:36 p.m. |
| PD | Predicate disambiguation | batch_69e6f643ca74819083e8ab78e843f243 |
completed | April 21, 2026, 4 a.m. |
Created at: April 16, 2026, 8:27 p.m.