Triple
T2764110
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Steinmetz’s law of hysteresis |
E61292
|
entity |
| Predicate | lossType |
P43002
|
FINISHED |
| Object | hysteresis loss in magnetic cores |
—
|
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: hysteresis loss in magnetic cores | Statement: [Steinmetz’s law of hysteresis, lossType, hysteresis loss in magnetic cores]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lossType Context triple: [Steinmetz’s law of hysteresis, lossType, hysteresis loss in magnetic cores]
-
A.
losses
Indicates that an entity experiences a decrease in value, quantity, or advantage as a result of some event or comparison.
-
B.
significantLoss
Indicates that an entity has experienced a major or substantial decrease in value, quantity, or status beyond a normal or minor loss.
-
C.
lost
Indicates that an entity no longer possesses or has been deprived of another entity it previously had.
-
D.
loserPoints
Indicates the number of points awarded to or accumulated by the losing side in a competitive event or comparison.
-
E.
usesLossFunction
Indicates that one entity employs a particular loss function as part of its optimization or learning process.
- 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_69ab4b7bab6c8190a5c2efef19a8ef34 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abddceb9d88190961e30d521a21552 |
completed | March 7, 2026, 8:11 a.m. |
| PD | Predicate disambiguation | batch_69abdcfc5e1c8190a5ac2c48d3eaeb0a |
completed | March 7, 2026, 8:08 a.m. |
| PDg | Predicate description generation | batch_69abddcc348081908b5f760899389d4f |
completed | March 7, 2026, 8:11 a.m. |
Created at: March 6, 2026, 9:57 p.m.