Triple
T4065626
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Himawari meteorological satellites |
E86315
|
entity |
| Predicate | temporalResolution |
P38785
|
FINISHED |
| Object | high-frequency full-disk imagery |
—
|
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: high-frequency full-disk imagery | Statement: [Himawari meteorological satellites, temporalResolution, high-frequency full-disk imagery]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: temporalResolution Context triple: [Himawari meteorological satellites, temporalResolution, high-frequency full-disk imagery]
-
A.
timeSampling
chosen
Indicates that one entity specifies how or at what intervals another entity is sampled or measured over time.
-
B.
temporalAspect
Indicates the time-related characteristics or phase (such as duration, frequency, or temporal status) associated with an event or relationship.
-
C.
timeCharacteristic
Indicates a relationship where one entity specifies a temporal property, feature, or constraint that characterizes another entity or event.
-
D.
timePeriod
Indicates the specific span or interval of time during which an event, state, or relationship occurs or is valid.
-
E.
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.
- 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_69aed93c69208190a4efac0efe3cd69b |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefd0bdea48190805a79515ee92709 |
completed | March 9, 2026, 5:02 p.m. |
| PD | Predicate disambiguation | batch_69aef90438908190a005b08ba271eacf |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:38 p.m.