Triple
T3575939
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alexanderson alternator |
E75687
|
entity |
| Predicate | modulationMethod |
P23647
|
FINISHED |
| Object | on-off keying for Morse code |
—
|
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: on-off keying for Morse code | Statement: [Alexanderson alternator, modulationMethod, on-off keying for Morse code]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: modulationMethod Context triple: [Alexanderson alternator, modulationMethod, on-off keying for Morse code]
-
A.
usesModulation
chosen
Indicates that one entity applies or employs a particular modulation method or scheme in relation to another entity or process.
-
B.
tuningMethod
Indicates the method or approach used to adjust or optimize something’s parameters or performance.
-
C.
usesBasebandTransmission
Indicates that one entity employs baseband transmission, meaning it sends digital signals directly over a communication medium without modulating them onto a higher-frequency carrier.
-
D.
hasCarrierFrequency
Indicates that an entity (such as a signal or transmission) is associated with a specific carrier frequency at which it is transmitted or modulated.
-
E.
decodingMethod
Indicates the technique or process used to convert encoded or encrypted data back into its original, interpretable form.
- 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_69ad85d5e3008190bdfe0bacdd1f5a1b |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc0da77008190922f414b85b9cad4 |
completed | March 8, 2026, 6:32 p.m. |
| PD | Predicate disambiguation | batch_69adb83810c481909c645c08b978edc1 |
completed | March 8, 2026, 5:56 p.m. |
Created at: March 8, 2026, 3:21 p.m.