Triple
T10023448
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bayesian networks |
E200666
|
entity |
| Predicate | nodeRepresents |
P39361
|
FINISHED |
| Object | random variable |
—
|
LITERAL 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: random variable | Statement: [Bayesian networks, nodeRepresents, random variable]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nodeRepresents Context triple: [Bayesian networks, nodeRepresents, random variable]
-
A.
componentRepresents
chosen
Indicates that one component stands in for, symbolizes, or models another entity or concept within a system or context.
-
B.
hasRepresentationIn
Indicates that one entity is represented, depicted, or encoded within another entity, such as a concept, object, or data structure having a corresponding representation in a specific medium or context.
-
C.
fieldRepresents
Indicates that one field or attribute stands for, encodes, or symbolizes another concept, value, or entity.
-
D.
canRepresent
Indicates that one entity is capable of serving as a valid stand-in, proxy, or expression for another entity in a given context.
-
E.
representsAs
Indicates that one entity serves as a depiction, symbol, or stand-in for another entity in some representational context.
- F. None of above.
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. |
| PD | Predicate disambiguation | batch_69cd4b7cd4208190b2253583ee2f892c |
completed | April 1, 2026, 4:44 p.m. |
Created at: March 30, 2026, 8:53 p.m.