Triple
T18021277
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Parasympathetic nervous system |
E431121
|
entity |
| Predicate | effectOnLens |
P129475
|
FINISHED |
| Object | accommodation for near vision |
—
|
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: accommodation for near vision | Statement: [Parasympathetic nervous system, effectOnLens, accommodation for near vision]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectOnLens Context triple: [Parasympathetic nervous system, effectOnLens, accommodation for near vision]
-
A.
lensType
Indicates the specific kind or category of lens associated with or used by an entity.
-
B.
originalLens
Indicates that one lens is the initial or source lens from which another lens or lens configuration is derived or referenced.
-
C.
laterLensType
Indicates that one lens type occurs or is used at a later time than another lens type in a temporal sequence.
-
D.
usesLensMount
Indicates that one device or component is designed to accept, attach to, or operate with a specific type of lens mount.
-
E.
resolutionEffect
Indicates the outcome, consequence, or change that results from a particular resolution, decision, or problem-solving action.
- 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_69d8b904530081908bf341d842464856 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4b9c299c48190b0cceecf77cb6de9 |
completed | April 19, 2026, 11:17 a.m. |
| PD | Predicate disambiguation | batch_69e3f904b8048190add43883cd7cb191 |
completed | April 18, 2026, 9:35 p.m. |
| PDg | Predicate description generation | batch_69e42d8eefa88190a700c7c1b4213e46 |
completed | April 19, 2026, 1:19 a.m. |
Created at: April 10, 2026, 10:24 a.m.