Triple
T24929039
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MPEG-2 |
E618936
|
entity |
| Predicate | videoCompressionType |
P27672
|
FINISHED |
| Object | lossy compression |
—
|
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: lossy compression | Statement: [MPEG-2, videoCompressionType, lossy compression]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: videoCompressionType Context triple: [MPEG-2, videoCompressionType, lossy compression]
-
A.
videoEncoding
Indicates that one entity is used to encode, compress, or transform video data into a particular digital format or representation for another entity.
-
B.
defaultFileFormatVideo
Indicates the default file format used for saving or exporting video content.
-
C.
videoStandard
Indicates the video format or broadcasting standard that applies to a given video or recording.
-
D.
compressionType
chosen
Indicates the method or format used to compress data or content in the relationship.
-
E.
videoQuality
Indicates the level or standard of clarity, resolution, and overall visual fidelity associated with a given video.
- 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_69e2fab9edd88190b86004a78a28bc20 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f423b2a8f88190b0b0ce3db1c97f6f |
completed | May 1, 2026, 3:53 a.m. |
| PD | Predicate disambiguation | batch_69f4210130d08190ae30b7943f7a0bbc |
completed | May 1, 2026, 3:41 a.m. |
Created at: April 18, 2026, 5:29 a.m.