Triple
T26629225
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SAXO |
E668445
|
entity |
| Predicate | wavefrontSensorType |
P161944
|
FINISHED |
| Object |
Shack–Hartmann wavefront sensor
The Shack–Hartmann wavefront sensor is an optical device that measures wavefront distortions by sampling incoming light with a microlens array to enable precise correction in adaptive optics systems.
|
E1734870
|
NE FINISHED |
How this triple was built (3 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: Shack–Hartmann wavefront sensor | Statement: [SAXO, wavefrontSensorType, Shack–Hartmann wavefront sensor]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Shack–Hartmann wavefront sensor Triple: [SAXO, wavefrontSensorType, Shack–Hartmann wavefront sensor]
Generated description
The Shack–Hartmann wavefront sensor is an optical device that measures wavefront distortions by sampling incoming light with a microlens array to enable precise correction in adaptive optics systems.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wavefrontSensorType Context triple: [SAXO, wavefrontSensorType, Shack–Hartmann wavefront sensor]
-
A.
waveType
Indicates the specific kind or category of wave associated with an entity or interaction (e.g., type of signal, motion, or oscillation).
-
B.
sightType
Indicates the specific kind or category of sight or visual perception associated with an entity or event.
-
C.
viewfinderType
Indicates the type or kind of viewfinder associated with or used by an entity.
-
D.
sensoryModality
Indicates the type of sensory channel (e.g., visual, auditory, tactile) through which an experience, perception, or information is received or processed.
-
E.
lensType
Indicates the specific kind or category of lens associated with or used by an entity.
- F. None of above. chosen
Provenance (7 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_69ee9cff507c819092b95bf7219a702e |
completed | April 26, 2026, 11:17 p.m. |
| NER | Named-entity recognition | batch_69f62081136c81909de8090b1d6c1ac5 |
completed | May 2, 2026, 4:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a11ec3663e881908ea934e45c990b7c |
completed | May 23, 2026, 6:04 p.m. |
| NEDg | Description generation | batch_6a11ecc2d59c8190812339ba67cc0549 |
completed | May 23, 2026, 6:06 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a11ed49546081908d4553f4ceab71ab |
completed | May 23, 2026, 6:09 p.m. |
| PD | Predicate disambiguation | batch_69f61b3d23f481908dfec27adace900a |
completed | May 2, 2026, 3:41 p.m. |
| PDg | Predicate description generation | batch_69f61fd5442081908ca677a9c81dcb3f |
completed | May 2, 2026, 4:01 p.m. |
Created at: April 27, 2026, 2:24 a.m.