Triple
T4032074
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Phoenix (infrared spectrograph) |
E83736
|
entity |
| Predicate | resolutionCharacteristic |
P40836
|
FINISHED |
| Object | high spectral resolution |
—
|
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 spectral resolution | Statement: [Phoenix (infrared spectrograph), resolutionCharacteristic, high spectral resolution]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: resolutionCharacteristic Context triple: [Phoenix (infrared spectrograph), resolutionCharacteristic, high spectral resolution]
-
A.
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.
-
B.
displayResolution
Indicates the relationship specifying the width and height dimensions at which visual content is rendered or shown on a display.
-
C.
resolutionClass
Indicates the category or type of resolution applied to address or conclude a particular issue, conflict, or process.
-
D.
resolutionDevice
Indicates a device or instrument that is used to resolve, determine, or measure the outcome or value associated with another entity or process.
-
E.
sensorResolution
chosen
Indicates the level of detail or precision with which a sensor can measure or distinguish changes in the observed quantity or environment.
- 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_69aed92e29ac819080f7a98b594fec05 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefb0f776881909db6b7df1db7664c |
completed | March 9, 2026, 4:53 p.m. |
| PD | Predicate disambiguation | batch_69aef8fe440c819093a7fa22c4ff3f1a |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:36 p.m.