Triple
T14369215
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harold Jeffreys |
E356313
|
entity |
| Predicate | knownFor |
P22
|
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: [Harold Jeffreys, knownFor, Jeffreys prior]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeffreys prior Context triple: [Harold Jeffreys, knownFor, 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
Bayes is a surname most famously associated with Thomas Bayes, the 18th-century statistician and minister whose work led to the development of Bayesian probability theory.
-
C.
Bésayes
Bésayes is a small commune in southeastern France’s Drôme department, known for its rural setting at the foot of the Vercors massif.
-
D.
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.
-
E.
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.
- 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_69d8279163a081908aec45c0e3f1e02f |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de8fb0b8988190ab834a85911c015c |
completed | April 14, 2026, 7:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd4c51bf888190b1776461884c4514 |
completed | May 8, 2026, 2:37 a.m. |
Created at: April 10, 2026, 1:15 a.m.