Triple
T17521167
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TD3 |
E426680
|
entity |
| Predicate | usesClippedNoise |
P127780
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [TD3, usesClippedNoise, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesClippedNoise Context triple: [TD3, usesClippedNoise, true]
-
A.
hasNoiseTerm
Indicates that a given expression, model, or equation includes an additional noise term representing random or unexplained variation.
-
B.
hasNoisePerformance
Indicates the degree to which one entity’s operation or behavior produces or is characterized by a certain level or quality of noise.
-
C.
hasNoiseModes
Indicates that an entity supports or is associated with one or more distinct noise-related operating modes or settings.
-
D.
hasClip
Indicates that one entity possesses, contains, or is associated with a clip (such as a video segment, audio snippet, or attached fastener).
-
E.
hasNoiseContext
Indicates that an entity is associated with or occurs within a particular noise-related environment or acoustic condition.
- 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_69d889de677081909b22d2657b1f0292 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e452d2f79881909556894728e255ab |
completed | April 19, 2026, 3:58 a.m. |
| PD | Predicate disambiguation | batch_69e3b4f8b9888190aa8a45e09acf4319 |
completed | April 18, 2026, 4:44 p.m. |
| PDg | Predicate description generation | batch_69e3bbb37d148190b7f38599c06594ee |
completed | April 18, 2026, 5:13 p.m. |
Created at: April 10, 2026, 5:49 a.m.