Triple
T9634821
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Larmor precession |
E232901
|
entity |
| Predicate | frequencyProportionalTo |
P13344
|
FINISHED |
| Object | magnetic field strength |
—
|
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: magnetic field strength | Statement: [Larmor precession, frequencyProportionalTo, magnetic field strength]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: frequencyProportionalTo Context triple: [Larmor precession, frequencyProportionalTo, magnetic field strength]
-
A.
frequencyDependsOn
chosen
Indicates that the frequency of one event, action, or state is determined or influenced by another factor or condition.
-
B.
frequencyComparedTo
Indicates how often one event or action occurs relative to another, expressing a comparison of their frequencies.
-
C.
frequency
Indicates how often an event, action, or relationship occurs within a given period or context.
-
D.
frequencyShiftProperty
Indicates a relationship where one entity specifies or characterizes the amount or nature of a change in frequency experienced by another entity or signal.
-
E.
frequencyClass
Indicates how often an event, action, or relation occurs, typically by assigning it to a predefined frequency category or class.
- 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_69ca848940cc8190b97cec654cb3bb4a |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9b2a0e2c8190ab5aaa223b1e1cde |
completed | April 1, 2026, 10:24 p.m. |
| PD | Predicate disambiguation | batch_69ccd5acfa5c8190aaba3cf548723604 |
completed | April 1, 2026, 8:22 a.m. |
Created at: March 30, 2026, 8:11 p.m.