Triple
T23269375
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Large Volume Detector |
E588245
|
entity |
| Predicate | backgroundReduction |
P107588
|
FINISHED |
| Object | cosmic-ray muon shielding by rock overburden |
—
|
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: cosmic-ray muon shielding by rock overburden | Statement: [Large Volume Detector, backgroundReduction, cosmic-ray muon shielding by rock overburden]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: backgroundReduction Context triple: [Large Volume Detector, backgroundReduction, cosmic-ray muon shielding by rock overburden]
-
A.
backgroundSuppression
Indicates that non-essential or contextual background elements are reduced, filtered out, or deemphasized relative to the primary subject or signal.
-
B.
noiseReductionFeature
Indicates that an entity includes or supports a capability to reduce or minimize unwanted noise.
-
C.
noiseReductionType
chosen
Indicates the specific method or technique used to reduce or minimize noise in a given context.
-
D.
targetReduction
Indicates a relationship where one entity is intended or expected to decrease, diminish, or lessen another entity by a specified amount or proportion.
-
E.
noiseReductionGoal
Indicates the intended target level or objective for reducing noise in a given context or system.
- 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_69e25d148adc819088efbf42672604e9 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1957219188190b30bceffad1542da |
completed | April 29, 2026, 5:21 a.m. |
| PD | Predicate disambiguation | batch_69effcecabd88190856fb6e1d993e4dd |
completed | April 28, 2026, 12:18 a.m. |
Created at: April 17, 2026, 4:45 p.m.