Triple
T806556
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cassegrain focus |
E17448
|
entity |
| Predicate | hasFocalPlane |
P21256
|
FINISHED |
| Object | behind primary mirror cell |
—
|
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: behind primary mirror cell | Statement: [Cassegrain focus, hasFocalPlane, behind primary mirror cell]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFocalPlane Context triple: [Cassegrain focus, hasFocalPlane, behind primary mirror cell]
-
A.
hasFocalRatioRange
Indicates that an entity is associated with a range of possible focal ratios, specifying the minimum and maximum f-number values it can have.
-
B.
hasAperture
Indicates that one entity possesses or is characterized by a specific opening, gap, or aperture.
-
C.
hasFieldOfView
Indicates that one entity possesses a visual coverage area within which it can perceive or detect other entities or regions.
-
D.
hasApertureClass
Indicates that one entity is classified according to a specific aperture category or class of another entity.
-
E.
meetsInCamera
Indicates that two or more entities are physically present together in the same camera frame or shot at the same time.
- 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_69a4937ae8a08190b5084a03d532b30e |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4ac07fedc8190ab05595f25c1792f |
completed | March 1, 2026, 9:13 p.m. |
| PD | Predicate disambiguation | batch_69a4aa7221c081908068e66fe720f26d |
completed | March 1, 2026, 9:06 p.m. |
| PDg | Predicate description generation | batch_69a4ac0688708190b62ac0a8239ec8c8 |
completed | March 1, 2026, 9:13 p.m. |
Created at: March 1, 2026, 7:38 p.m.