Triple
T12370083
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Progressive GAN |
E294977
|
entity |
| Predicate | introducedBy |
P513
|
FINISHED |
| Object | Samuli Laine |
E975239
|
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: Samuli Laine | Statement: [Progressive GAN, introducedBy, Samuli Laine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Samuli Laine Context triple: [Progressive GAN, introducedBy, Samuli Laine]
-
A.
Samuli Laine
chosen
Samuli Laine is a computer graphics and machine learning researcher known for his work at NVIDIA, including co-developing the influential StyleGAN generative adversarial network architecture.
-
B.
Teemu Laajasalo
Teemu Laajasalo is a Finnish Lutheran bishop and theologian who serves as the Bishop of Helsinki in the Evangelical Lutheran Church of Finland.
-
C.
Teemu Hartikainen
Teemu Hartikainen is a Finnish professional ice hockey forward known for his strong play in the KHL and a brief stint in the NHL with the Edmonton Oilers.
-
D.
Timo Sarpaneva
Timo Sarpaneva was a renowned Finnish designer and glass artist celebrated for his innovative, modernist creations that helped define Scandinavian design in the 20th century.
-
E.
Timo Aila
Timo Aila is a computer scientist and researcher at NVIDIA known for his influential work in computer graphics and deep learning, including co-developing the StyleGAN generative adversarial network architecture.
- 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_69d6ab6d8a4081908636601e69ddf262 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93fa65a608190a1597a49751185a8 |
completed | April 10, 2026, 6:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f65ea0a4c4819091a7a66c3b73d776 |
completed | May 2, 2026, 8:29 p.m. |
Created at: April 8, 2026, 9:54 p.m.