Triple
T10023440
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bayesian networks |
E200666
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | Bayes networks |
E200666
|
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: Bayes networks | Statement: [Bayesian networks, alsoKnownAs, Bayes networks]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bayes networks Context triple: [Bayesian networks, alsoKnownAs, Bayes networks]
-
A.
Bayesian networks
chosen
Bayesian networks are probabilistic graphical models that represent variables and their conditional dependencies using directed acyclic graphs, enabling structured reasoning and inference under uncertainty.
-
B.
Probabilistic Graphical Models: Principles and Techniques
Probabilistic Graphical Models: Principles and Techniques is a foundational textbook that systematically presents the theory, algorithms, and applications of probabilistic graphical models in machine learning and artificial intelligence.
-
C.
Bayesian inference
Bayesian inference is a statistical framework that updates the probability of hypotheses as more evidence or data becomes available, using Bayes’ theorem to combine prior beliefs with observed information.
-
D.
Bayes rules
Bayes rules are decision rules in statistical decision theory that minimize expected loss with respect to a prior distribution, forming a central concept in Bayesian optimal decision-making.
-
E.
Boltzmann machines
Boltzmann machines are stochastic recurrent neural networks used for learning complex probability distributions, foundational in unsupervised learning and energy-based models.
- 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_69ca831c45f08190ac1505cc15076608 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cdcd7c75548190aa604d90d63dc111 |
completed | April 2, 2026, 1:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d26abb0ab08190b5bcf101c5680f3c |
completed | April 5, 2026, 1:59 p.m. |
Created at: March 30, 2026, 8:53 p.m.