Triple
T32669181
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bayesian model averaging |
E835242
|
entity |
| Predicate | canUseApproximation |
P174783
|
FINISHED |
| Object | Bayesian information criterion |
—
|
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: Bayesian information criterion | Statement: [Bayesian model averaging, canUseApproximation, Bayesian information criterion]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: canUseApproximation Context triple: [Bayesian model averaging, canUseApproximation, Bayesian information criterion]
-
A.
hasApproximateUse
Indicates that one entity is used for a purpose that is similar to, but not exactly the same as, the use or function of another entity.
-
B.
usedFromApprox
Indicates that something has been used starting from an approximate point in time or condition, rather than from a precisely defined one.
-
C.
hasApproximateValue
Indicates that one entity’s value is close to, but not exactly equal to, the value of another entity within an acceptable margin of error.
-
D.
approximationType
Indicates the specific method or scheme used to approximate a value, function, or relationship in a given context.
-
E.
hasRandomizedApproximation
Indicates that there exists a randomized algorithm or method that can approximate the result of the referenced entity or process within some probabilistic accuracy or error bounds.
- F. None of above. chosen
Provenance (4 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_69f349303ccc8190a70d0f6e8a21d3fb |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6c7e32ec08190b74856937c4a9fc3 |
completed | May 3, 2026, 3:58 a.m. |
| PD | Predicate disambiguation | batch_69f6c3f617c08190a70ba880210f908c |
completed | May 3, 2026, 3:41 a.m. |
| PDg | Predicate description generation | batch_69f6c77500a08190b2bdeca33bd2ac08 |
completed | May 3, 2026, 3:56 a.m. |
Created at: May 1, 2026, 1:08 a.m.