Triple
T27752934
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | COSTAR |
E701257
|
entity |
| Predicate | correctedAberrationType |
P133462
|
FINISHED |
| Object | spherical aberration |
—
|
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: spherical aberration | Statement: [COSTAR, correctedAberrationType, spherical aberration]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: correctedAberrationType Context triple: [COSTAR, correctedAberrationType, spherical aberration]
-
A.
aberrationTypeAddressed
chosen
Indicates the specific type of aberration or deviation that is being addressed or corrected by an action or process.
-
B.
aberrationClass
Indicates a classification relationship where an entity is assigned to a specific type or category of aberration.
-
C.
hasAberrationCharacteristics
Indicates that an entity exhibits traits or properties that deviate from what is considered normal, standard, or expected.
-
D.
aberrationCorrection
Indicates the application or presence of a process that corrects optical or imaging aberrations in a system or setup.
-
E.
alterationType
Indicates the specific kind or category of change or modification that has been applied to an entity or relationship.
- 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_69ef6a5193808190816eb7d0020b2d87 |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69f6691f5e188190b12c7b2eb729a45e |
completed | May 2, 2026, 9:14 p.m. |
| PD | Predicate disambiguation | batch_69f66598d6008190a7ca8ff80399fd34 |
completed | May 2, 2026, 8:59 p.m. |
Created at: April 27, 2026, 4:21 p.m.