Triple
T3892897
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GeMS |
E88100
|
entity |
| Predicate | usesWavefrontSensors |
P7239
|
FINISHED |
| Object | multiple wavefront sensors |
—
|
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: multiple wavefront sensors | Statement: [GeMS, usesWavefrontSensors, multiple wavefront sensors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesWavefrontSensors Context triple: [GeMS, usesWavefrontSensors, multiple wavefront sensors]
-
A.
hasNightVision
Indicates that an entity possesses the ability to see effectively in low-light or dark conditions.
-
B.
usesOpticsType
chosen
Indicates that one entity employs or is characterized by a specific type of optical system or technology.
-
C.
hasSpectrograph
Indicates that one entity is equipped with or contains a spectrograph instrument.
-
D.
hasFieldOfView
Indicates that one entity possesses a visual coverage area within which it can perceive or detect other entities or regions.
-
E.
supportsHardwareRayTracing
Indicates that one entity provides or enables hardware-level ray tracing capabilities for another entity or within a given context.
- 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_69aed9466d548190939f5217a23ed4ac |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aef1abe2dc81909c18aeae9b286898 |
completed | March 9, 2026, 4:13 p.m. |
| PD | Predicate disambiguation | batch_69aee75b5b808190a348a31b1325d3d0 |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:21 p.m.