Triple
T4651225
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GPT-3 |
E102297
|
entity |
| Predicate | hasAuthor |
P4244
|
FINISHED |
| Object | Dario Amodei |
E99318
|
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: Dario Amodei | Statement: [GPT-3, hasAuthor, Dario Amodei]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dario Amodei Context triple: [GPT-3, hasAuthor, Dario Amodei]
-
A.
Dario Amodei
chosen
Dario Amodei is an AI researcher and entrepreneur, co-founder and CEO of Anthropic and former OpenAI research leader known for his work on large language models and AI safety.
-
B.
Pieter Abbeel
Pieter Abbeel is a Belgian-American computer scientist and professor at UC Berkeley known for his influential work in robotics and deep reinforcement learning.
-
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.
Ilya Sutskever
Ilya Sutskever is a leading artificial intelligence researcher and co-founder of OpenAI, known for his pioneering work in deep learning and neural networks.
-
E.
Wolfram Burgard
Wolfram Burgard is a German computer scientist and roboticist known for his influential work in probabilistic robotics, autonomous navigation, and artificial intelligence.
- 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_69bd43d71a308190afea7280841b0de8 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd630343f88190954d19fcd18a5864 |
completed | March 20, 2026, 3:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be43936630819094417faf4df7f6df |
completed | March 21, 2026, 7:06 a.m. |
Created at: March 20, 2026, 1:14 p.m.