Triple
T16736802
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | David Donoho |
E406737
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Donoho |
E406737
|
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: Donoho | Statement: [David Donoho, familyName, Donoho]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Donoho Context triple: [David Donoho, familyName, Donoho]
-
A.
David Donoho
chosen
David Donoho is an American mathematician renowned for his foundational contributions to statistics, signal processing, and compressed sensing.
-
B.
Ingrid Daubechies
Ingrid Daubechies is a Belgian physicist and mathematician renowned for her pioneering work in wavelet theory and its applications to signal processing, image compression, and data analysis.
-
C.
Daubechies wavelets
Daubechies wavelets are a family of compactly supported orthogonal wavelets widely used in signal processing and image compression for their efficient time-frequency localization.
-
D.
Candes-Saint-Martin
Candes-Saint-Martin is a picturesque historic village in central France, known for its medieval architecture and scenic location at the confluence of the Vienne and Loire rivers.
-
E.
David Taubman
David Taubman is an electrical engineer and academic best known for his contributions to image compression and the development of the JPEG2000 standard.
- 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_69d8838ffb088190a0b11149929006bf |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e39c3a86848190a03f243dd1bdb899 |
completed | April 18, 2026, 2:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00a51c5c388190a88f8bd67dbac82e |
completed | May 10, 2026, 3:32 p.m. |
Created at: April 10, 2026, 5:20 a.m.