Triple
T1463156
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gauss's law for magnetism |
E31558
|
entity |
| Predicate | involvesOperator |
P28828
|
FINISHED |
| Object | divergence operator |
—
|
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: divergence operator | Statement: [Gauss's law for magnetism, involvesOperator, divergence operator]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: involvesOperator Context triple: [Gauss's law for magnetism, involvesOperator, divergence operator]
-
A.
otherOperator
Indicates a relationship where one operator is distinguished from, or serves as an alternative to, another operator within the same context or system.
-
B.
typicalOperatorType
Indicates the usual or most common type or category of operator associated with a given entity or context.
-
C.
usedByOperator
Indicates that something is utilized or operated by a particular operator or operating entity.
-
D.
operator
Indicates that one entity functions as the operator (controller or handler) of another entity, such as a system, device, or process.
-
E.
operationOf
Indicates that one entity is the function, activity, or process carried out by another entity (such as a system, device, or organization).
- 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_69a49917dfc081909acdbdf5d684f1ef |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c5b89708819084fb9ba4ff293b8b |
completed | March 1, 2026, 11:03 p.m. |
| PD | Predicate disambiguation | batch_69a4c48121e48190946c23c583e5fb64 |
completed | March 1, 2026, 10:58 p.m. |
| PDg | Predicate description generation | batch_69a4c55508948190922aee3230a4323e |
completed | March 1, 2026, 11:01 p.m. |
Created at: March 1, 2026, 8 p.m.