Triple
T17693744
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Actor-Critic using Kronecker-Factored Trust Region |
E441103
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object | A3C |
—
|
NE NERFINISHED |
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: A3C | Statement: [Actor-Critic using Kronecker-Factored Trust Region, relatedTo, A3C]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: A3C Context triple: [Actor-Critic using Kronecker-Factored Trust Region, relatedTo, A3C]
-
A.
A3C
chosen
A3C (Asynchronous Advantage Actor-Critic) is a reinforcement learning algorithm that trains multiple parallel agents to learn policies and value functions efficiently using asynchronous gradient updates.
-
B.
A2C
A2C (Advantage Actor-Critic) is a popular synchronous policy gradient reinforcement learning algorithm that combines value-based and policy-based methods to improve training stability and efficiency.
-
C.
A-3
The A-3 is a major Spanish motorway that connects Madrid with Valencia and the eastern Mediterranean coast.
-
D.
A33
A33 is a major road in southern England that runs between Reading and Southampton, serving as an important regional transport route.
-
E.
AIC
AIC is the collective term for Australia’s national intelligence agencies responsible for gathering, assessing, and disseminating security and foreign intelligence.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9e940b081908b862bb0e6e89b0d |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4715485d88190b9b6f347ff85d7c7 |
completed | April 19, 2026, 6:08 a.m. |
Created at: April 10, 2026, 10:04 a.m.