Triple
T9744703
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | COBE Differential Microwave Radiometer |
E236276
|
entity |
| Predicate | noiseReductionTechnique |
P44489
|
FINISHED |
| Object | differential radiometry |
—
|
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: differential radiometry | Statement: [COBE Differential Microwave Radiometer, noiseReductionTechnique, differential radiometry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: noiseReductionTechnique Context triple: [COBE Differential Microwave Radiometer, noiseReductionTechnique, differential radiometry]
-
A.
noiseReductionFeature
chosen
Indicates that an entity includes or supports a capability to reduce or minimize unwanted noise.
-
B.
noiseReductionGoal
Indicates the intended target level or objective for reducing noise in a given context or system.
-
C.
hasNoisePerformance
Indicates the degree to which one entity’s operation or behavior produces or is characterized by a certain level or quality of noise.
-
D.
noiseLevel
Indicates the intensity or amount of sound present in a given environment or from a specific source.
-
E.
hasNoiseModes
Indicates that an entity supports or is associated with one or more distinct noise-related operating modes or settings.
- 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_69ca84d3e24481908a476e2231123cf9 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9f2f8e648190ad94c940f9dc1de0 |
completed | April 1, 2026, 10:41 p.m. |
| PD | Predicate disambiguation | batch_69cd03cc128c81908b84ef224f858b4e |
completed | April 1, 2026, 11:38 a.m. |
Created at: March 30, 2026, 8:23 p.m.