Triple
T1793130
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AlphaFold |
E39542
|
entity |
| Predicate | majorUpdate |
P31455
|
FINISHED |
| Object | AlphaFold2 |
E39542
|
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: AlphaFold2 | Statement: [AlphaFold, majorUpdate, AlphaFold2]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: AlphaFold2 Context triple: [AlphaFold, majorUpdate, AlphaFold2]
-
A.
AlphaFold
chosen
AlphaFold is an artificial intelligence system developed by DeepMind that predicts protein 3D structures from amino acid sequences with unprecedented accuracy, revolutionizing structural biology.
-
B.
EMBL-EBI AlphaFold Protein Structure Database
The EMBL-EBI AlphaFold Protein Structure Database is a publicly accessible resource providing predicted 3D structures of proteins generated by DeepMind’s AlphaFold system.
-
C.
CASP (Critical Assessment of protein Structure Prediction)
CASP (Critical Assessment of protein Structure Prediction) is a biennial community-wide experiment and benchmark that objectively evaluates and compares the accuracy of computational methods for predicting protein structures.
-
D.
GPT-2
GPT-2 is a large transformer-based language model known for generating coherent, human-like text and sparking widespread discussion about the implications of advanced AI text generation.
-
E.
MuZero
MuZero is a DeepMind reinforcement learning algorithm that learns to plan and master complex games like Go, chess, and Atari without being given the rules in advance.
- 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_69a88631854081909723959921e45c2b |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abaffee0f88190aa7a42ef4a4e2bd2 |
completed | March 7, 2026, 4:56 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adbf54330c81908046b519a0297760 |
completed | March 8, 2026, 6:26 p.m. |
Created at: March 4, 2026, 7:32 p.m.