Triple
T13566774
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daubechies wavelets |
E324056
|
entity |
| Predicate | hasMember |
P10
|
FINISHED |
| Object | D8 wavelet |
E324056
|
NE 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: D8 wavelet | Statement: [Daubechies wavelets, hasMember, D8 wavelet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: D8 wavelet Context triple: [Daubechies wavelets, hasMember, D8 wavelet]
-
A.
Daubechies wavelets
chosen
Daubechies wavelets are a family of compactly supported orthogonal wavelets widely used in signal processing and image compression for their efficient time-frequency localization.
-
B.
CWT
CWT is the IATA airport code for Cowra Airport, a regional airport serving the town of Cowra in New South Wales, Australia.
-
C.
D8
D8 is the commonly used abbreviation for California Department of Transportation's District 8, which oversees state highways and transportation infrastructure in parts of Southern California.
-
D.
Ten Lectures on Wavelets
Ten Lectures on Wavelets is a foundational monograph by Ingrid Daubechies that systematically introduces the theory and applications of wavelets in mathematics and signal processing.
-
E.
JPEG 2000
JPEG 2000 is an image compression standard that improves on the original JPEG by using wavelet-based compression to provide higher quality, better scalability, and advanced features such as lossless compression and region-of-interest coding.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d8076830b48190910a902bae5888e2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb00cecd48190a9a2caff3d424817 |
completed | April 12, 2026, 2:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f75db031d88190983e3ccd054082bd |
completed | May 3, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:48 p.m.