Triple
T8912647
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fisher information |
E212219
|
entity |
| Predicate | usedToDefine |
P773
|
FINISHED |
| Object | Jeffreys prior |
E765761
|
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: Jeffreys prior | Statement: [Fisher information, usedToDefine, Jeffreys prior]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeffreys prior Context triple: [Fisher information, usedToDefine, Jeffreys prior]
-
A.
Jeffreys prior
chosen
Jeffreys prior is an objective Bayesian prior distribution defined to be invariant under reparameterization by constructing it from the square root of the determinant of the Fisher information matrix.
-
B.
Bayes factor
The Bayes factor is a Bayesian model comparison metric that quantifies how much more strongly data support one statistical model or hypothesis over another.
-
C.
Bayes’ theorem
Bayes’ theorem is a fundamental result in probability theory that describes how to update the probability of a hypothesis based on new evidence.
-
D.
Dirichlet distribution
The Dirichlet distribution is a family of continuous multivariate probability distributions commonly used as a prior over categorical or multinomial parameters in Bayesian statistics.
-
E.
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.
- 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_69ca8393b1808190bd4336787ffa2c40 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc6525d1408190a76522d7c4ac37da |
completed | April 1, 2026, 12:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfc1cdfa6c8190af7b6312c73d59f9 |
completed | April 3, 2026, 1:34 p.m. |
Created at: March 30, 2026, 6:56 p.m.