Triple
T12207487
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CycleGAN |
E290871
|
entity |
| Predicate | introducedBy |
P513
|
FINISHED |
| Object | Alexei A. Efros |
E64553
|
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: Alexei A. Efros | Statement: [CycleGAN, introducedBy, Alexei A. Efros]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alexei A. Efros Context triple: [CycleGAN, introducedBy, Alexei A. Efros]
-
A.
Alexei Efros
chosen
Alexei Efros is a prominent computer scientist known for his influential work in computer vision and computational photography.
-
B.
Abraham Girshick
Abraham Girshick was an American statistician known for his contributions to statistical decision theory and his work during World War II with Columbia University's Statistical Research Group.
-
C.
Jonathon Shlens
Jonathon Shlens is a computer scientist and researcher known for his contributions to deep learning and computer vision, including influential work at Google.
-
D.
Christian Szegedy
Christian Szegedy is a computer scientist and AI researcher known for his influential work on deep learning and convolutional neural networks, including contributions to the Inception architecture.
-
E.
Karen Simonyan
Karen Simonyan is a computer scientist and deep learning researcher known for influential work in neural network architectures and generative models, including contributions to systems like WaveNet.
- 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_69d6ab65923081909acfc61b7a612233 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91c7d8f5c8190a46e9caa2a920fa9 |
completed | April 10, 2026, 3:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f60a9d2f0c81908352cd9f0167c6ab |
completed | May 2, 2026, 2:30 p.m. |
Created at: April 8, 2026, 9:51 p.m.