Triple
T17417290
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Context-Adaptive Binary Arithmetic Coding |
E423519
|
entity |
| Predicate | comparedWith |
P278
|
FINISHED |
| Object |
Context-Based Adaptive Variable Length Coding
Context-Based Adaptive Variable Length Coding (CAVLC) is an entropy coding method used in video compression standards like H.264/AVC that efficiently encodes transform coefficients using context-dependent variable-length codes.
|
E423517
|
NE FINISHED |
How this triple was built (4 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: Context-Based Adaptive Variable Length Coding | Statement: [Context-Adaptive Binary Arithmetic Coding, comparedWith, Context-Based Adaptive Variable Length Coding]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Context-Based Adaptive Variable Length Coding Context triple: [Context-Adaptive Binary Arithmetic Coding, comparedWith, Context-Based Adaptive Variable Length Coding]
-
A.
Context-Adaptive Binary Arithmetic Coding
Context-Adaptive Binary Arithmetic Coding (CABAC) is an advanced lossless entropy coding technique used in modern video compression standards to achieve high compression efficiency by modeling symbol probabilities with context.
-
B.
Versatile Video Coding
Versatile Video Coding is a next-generation video compression standard designed to significantly improve coding efficiency over its predecessors for applications ranging from low-bitrate streaming to ultra-high-definition video.
-
C.
Subband Codec
Subband Codec (SBC) is an audio compression format commonly used over Bluetooth for efficiently transmitting medium-quality stereo sound with relatively low computational complexity.
-
D.
H.26x family of video coding standards
The H.26x family of video coding standards is a series of internationally recognized compression formats (including H.261, H.262, H.263, H.264/AVC, and H.265/HEVC) developed primarily by ITU-T for efficient digital video transmission and storage.
-
E.
Lloyd’s algorithm
Lloyd’s algorithm is an iterative clustering method that partitions data into k groups by repeatedly assigning points to the nearest cluster center and updating those centers to minimize within-cluster variance.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Context-Based Adaptive Variable Length Coding Triple: [Context-Adaptive Binary Arithmetic Coding, comparedWith, Context-Based Adaptive Variable Length Coding]
Generated description
Context-Based Adaptive Variable Length Coding (CAVLC) is an entropy coding method used in video compression standards like H.264/AVC that efficiently encodes transform coefficients using context-dependent variable-length codes.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Context-Based Adaptive Variable Length Coding Target entity description: Context-Based Adaptive Variable Length Coding (CAVLC) is an entropy coding method used in video compression standards like H.264/AVC that efficiently encodes transform coefficients using context-dependent variable-length codes.
-
A.
Context-Adaptive Binary Arithmetic Coding
Context-Adaptive Binary Arithmetic Coding (CABAC) is an advanced lossless entropy coding technique used in modern video compression standards to achieve high compression efficiency by modeling symbol probabilities with context.
-
B.
Versatile Video Coding
Versatile Video Coding is a next-generation video compression standard designed to significantly improve coding efficiency over its predecessors for applications ranging from low-bitrate streaming to ultra-high-definition video.
-
C.
Subband Codec
Subband Codec (SBC) is an audio compression format commonly used over Bluetooth for efficiently transmitting medium-quality stereo sound with relatively low computational complexity.
-
D.
H.26x family of video coding standards
chosen
The H.26x family of video coding standards is a series of internationally recognized compression formats (including H.261, H.262, H.263, H.264/AVC, and H.265/HEVC) developed primarily by ITU-T for efficient digital video transmission and storage.
-
E.
Lloyd’s algorithm
Lloyd’s algorithm is an iterative clustering method that partitions data into k groups by repeatedly assigning points to the nearest cluster center and updating those centers to minimize within-cluster variance.
- F. None of above.
Provenance (5 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_69d889d7d27c819088486ce3f0627fa1 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e44233c7888190a4d2aa703b206851 |
completed | April 19, 2026, 2:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01a802363c8190bc0c78e78ddb013f |
completed | May 11, 2026, 9:57 a.m. |
| NEDg | Description generation | batch_6a01a898ceb48190ba3d40c898b17158 |
completed | May 11, 2026, 9:59 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a01a9183410819083c8239e3ce38ea2 |
completed | May 11, 2026, 10:02 a.m. |
Created at: April 10, 2026, 5:46 a.m.