Triple
T9175815
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DTS-HD Master Audio |
E220195
|
entity |
| Predicate | losslessEncodingScope |
P32205
|
FINISHED |
| Object | entire audio range including surround channels |
—
|
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: entire audio range including surround channels | Statement: [DTS-HD Master Audio, losslessEncodingScope, entire audio range including surround channels]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: losslessEncodingScope Context triple: [DTS-HD Master Audio, losslessEncodingScope, entire audio range including surround channels]
-
A.
encodingScope
Indicates the range or extent of content or information that is covered, represented, or captured by a particular encoding.
-
B.
compressionScope
chosen
Indicates the extent or range within which compression is applied to data or content.
-
C.
isLossless
Indicates that a transformation, compression, or process preserves all original information without any loss of data.
-
D.
encodingLibrary
Indicates that one entity is the software library or tool used to encode, transform, or serialize the other entity’s data or content.
-
E.
encodingStructure
Indicates the structural scheme or format used to encode information or data.
- 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_69ca83e589948190ac9907819db11ddf |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccbfa496548190a096969eebf732f3 |
completed | April 1, 2026, 6:48 a.m. |
| PD | Predicate disambiguation | batch_69cc660761d88190ab6134b43b376964 |
completed | April 1, 2026, 12:25 a.m. |
Created at: March 30, 2026, 7:23 p.m.