Triple
T28711005
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AVHRR |
E729830
|
entity |
| Predicate | typicalSpatialResolution |
P25684
|
FINISHED |
| Object | approximately 1.1 km at nadir |
—
|
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: approximately 1.1 km at nadir | Statement: [AVHRR, typicalSpatialResolution, approximately 1.1 km at nadir]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSpatialResolution Context triple: [AVHRR, typicalSpatialResolution, approximately 1.1 km at nadir]
-
A.
hasSpatialResolution
chosen
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.
typicalResolution
Indicates the usual or standard level of detail or clarity at which something (such as an image, display, or representation) is normally rendered or presented.
-
C.
typicalHorizontalAccuracy
Indicates the usual or expected degree of precision in the horizontal (x–y) position of a measurement or location estimate.
-
D.
sensorResolution
Indicates the level of detail or precision with which a sensor can measure or distinguish changes in the observed quantity or environment.
-
E.
typicalInputResolution
Indicates the usual or standard resolution at which input is expected or typically processed.
- 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_69f043e7d5a4819094b18aca10b1e024 |
completed | April 28, 2026, 5:21 a.m. |
| NER | Named-entity recognition | batch_6a0117b6539c8190be7d231e891fe546 |
completed | May 10, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_6a011762f7ec8190afc884b92419d33f |
completed | May 10, 2026, 11:40 p.m. |
Created at: April 28, 2026, 5:48 a.m.