Triple
T15637625
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Schmidt telescope |
E375985
|
entity |
| Predicate | fieldCorrectionMethod |
P58635
|
FINISHED |
| Object | aspheric corrector plate at entrance pupil |
—
|
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: aspheric corrector plate at entrance pupil | Statement: [Schmidt telescope, fieldCorrectionMethod, aspheric corrector plate at entrance pupil]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fieldCorrectionMethod Context triple: [Schmidt telescope, fieldCorrectionMethod, aspheric corrector plate at entrance pupil]
-
A.
requiresCorrection
Indicates that something is identified as needing modification, adjustment, or fixing to correct an error or deficiency.
-
B.
canBeCorrectedBy
Indicates that something has the potential to be made accurate, fixed, or improved through the intervention or action of a specified agent or method.
-
C.
includesCorrectionsFor
Indicates that one item contains modifications, fixes, or amendments that address errors or issues present in another item.
-
D.
canBeCorrectedTo
Indicates that one entity can be modified or adjusted so that it becomes equivalent to or matches another entity.
-
E.
aberrationCorrection
chosen
Indicates the application or presence of a process that corrects optical or imaging aberrations in a system or setup.
- 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_69d85cd035a48190b73d5579ab73969a |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04eba51f08190ac5d9de7fc89405a |
completed | April 16, 2026, 2:51 a.m. |
| PD | Predicate disambiguation | batch_69deda890140819082608931e993dd61 |
completed | April 15, 2026, 12:23 a.m. |
Created at: April 10, 2026, 4:14 a.m.