Triple
T147087
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ritchey–Chrétien reflector |
E3352
|
entity |
| Predicate | hasOpticalAxis |
P2518
|
FINISHED |
| Object | shared by primary and secondary mirrors |
—
|
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: shared by primary and secondary mirrors | Statement: [Ritchey–Chrétien reflector, hasOpticalAxis, shared by primary and secondary mirrors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOpticalAxis Context triple: [Ritchey–Chrétien reflector, hasOpticalAxis, shared by primary and secondary mirrors]
-
A.
hasFieldOfView
Indicates that one entity possesses a visual coverage area within which it can perceive or detect other entities or regions.
-
B.
hasApertureClass
Indicates that one entity is classified according to a specific aperture category or class of another entity.
-
C.
hasEquatorialRadius
Indicates that an entity has a specified radius measured at its equator.
-
D.
hasPhotonSphere
Indicates that an object (typically a massive, compact body) possesses a region where photons can orbit it on closed or nearly closed paths due to its gravitational field.
-
E.
telescopeType
chosen
Indicates the specific kind or category of telescope associated with an entity.
- 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_69a252868de4819080e21c9938bfe8b6 |
completed | Feb. 28, 2026, 2:27 a.m. |
| NER | Named-entity recognition | batch_69a258808ff08190a06b6206f635612b |
completed | Feb. 28, 2026, 2:52 a.m. |
| PD | Predicate disambiguation | batch_69a256580c2c8190beecca60ca8595f3 |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:31 a.m.