Triple
T1810074
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Australorp |
E40310
|
entity |
| Predicate | noiseLevel |
P32166
|
FINISHED |
| Object | relatively quiet |
—
|
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: relatively quiet | Statement: [Australorp, noiseLevel, relatively quiet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: noiseLevel Context triple: [Australorp, noiseLevel, relatively quiet]
-
A.
noiseReductionGoal
Indicates the intended target level or objective for reducing noise in a given context or system.
-
B.
soundHeardDistanceApproximate
Indicates that a sound was heard at an estimated, not precisely measured, distance from the listener or reference point.
-
C.
hasNoiseAbatementProcedures
Indicates that specific measures or procedures are in place to reduce or control noise associated with the related entity or activity.
-
D.
volume
Indicates the amount of three-dimensional space an entity occupies or contains.
-
E.
levels
Indicates that one entity adjusts, equalizes, or smooths out the height, intensity, or degree of another entity.
- 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_69a88643a3388190a612f2ebe1fb29e7 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab694d75ac8190a4d61399c04b9fb9 |
completed | March 6, 2026, 11:54 p.m. |
| PD | Predicate disambiguation | batch_69aa61d6b8ec8190a1597b2e44ea6534 |
completed | March 6, 2026, 5:10 a.m. |
| PDg | Predicate description generation | batch_69ab694bf6a08190a02ce2fc979e6701 |
completed | March 6, 2026, 11:54 p.m. |
Created at: March 4, 2026, 7:32 p.m.