Triple
T2139299
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wide Field Camera 3 |
E46723
|
entity |
| Predicate | hasFilterWheel |
P37043
|
FINISHED |
| Object | UVIS filter wheels |
—
|
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: UVIS filter wheels | Statement: [Wide Field Camera 3, hasFilterWheel, UVIS filter wheels]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFilterWheel Context triple: [Wide Field Camera 3, hasFilterWheel, UVIS filter wheels]
-
A.
hasAperture
Indicates that one entity possesses or is characterized by a specific opening, gap, or aperture.
-
B.
hasFocalPlane
Indicates that an optical system or imaging device possesses a specific focal plane where light is brought into focus.
-
C.
usesLensMount
Indicates that one device or component is designed to accept, attach to, or operate with a specific type of lens mount.
-
D.
hasApertureClass
Indicates that one entity is classified according to a specific aperture category or class of another entity.
-
E.
hasFieldOfView
Indicates that one entity possesses a visual coverage area within which it can perceive or detect other entities or regions.
- F. None of above. chosen
Provenance (4 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_69a88a174ab48190a5db20c132e5dccf |
completed | March 4, 2026, 7:37 p.m. |
| NER | Named-entity recognition | batch_69abbf74147c81908793c3694894f94a |
completed | March 7, 2026, 6:02 a.m. |
| PD | Predicate disambiguation | batch_69abbd96a3b0819081efbfef975e1513 |
completed | March 7, 2026, 5:54 a.m. |
| PDg | Predicate description generation | batch_69abbf71edf08190add69022aabfd49d |
completed | March 7, 2026, 6:02 a.m. |
Created at: March 4, 2026, 7:44 p.m.