Triple
T5259749
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Naiad |
E118793
|
entity |
| Predicate | surfaceFeaturesResolution |
P25684
|
FINISHED |
| Object | poorly resolved by Voyager 2 |
—
|
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: poorly resolved by Voyager 2 | Statement: [Naiad, surfaceFeaturesResolution, poorly resolved by Voyager 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: surfaceFeaturesResolution Context triple: [Naiad, surfaceFeaturesResolution, poorly resolved by Voyager 2]
-
A.
sensorResolution
Indicates the level of detail or precision with which a sensor can measure or distinguish changes in the observed quantity or environment.
-
B.
surfaceFeatureOf
Indicates that one entity is a surface-level characteristic, pattern, or feature belonging to or present on another entity.
-
C.
surfaceQuality
Indicates the condition or characteristics of an entity’s outer surface, such as its smoothness, roughness, or finish.
-
D.
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.
-
E.
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.
- 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_69bd446a42c88190b7ecbef006561d55 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7bcced6881909bdb7ac5471a37fe |
completed | March 20, 2026, 4:54 p.m. |
| PD | Predicate disambiguation | batch_69bd77c55224819096c0bcfcfae79bd3 |
completed | March 20, 2026, 4:37 p.m. |
Created at: March 20, 2026, 1:50 p.m.