Triple
T2979652
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | µA741 |
E80479
|
entity |
| Predicate | hasInputBiasCurrentTypical |
P44392
|
FINISHED |
| Object | 80 nA |
—
|
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: 80 nA | Statement: [µA741, hasInputBiasCurrentTypical, 80 nA]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasInputBiasCurrentTypical Context triple: [µA741, hasInputBiasCurrentTypical, 80 nA]
-
A.
hasOutputImpedance
Indicates that an entity (such as a device or component) exhibits a specific impedance at its output terminals.
-
B.
transistorCount
Indicates the number of transistors contained within an electronic component or device.
-
C.
beamCurrent
Indicates the amount of electric current carried by a directed beam (such as a particle or electron beam) in a system or experiment.
-
D.
circuitUsed
Indicates that a particular circuit is utilized or employed in performing an action, process, or function.
-
E.
hasPinCountDesignation
Indicates that an entity is assigned a specific designation describing the number of pins it has.
- 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_69ad8b15f6ac8190be5fd16a33edcb4f |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad999cca40819082e2d6d10bdb7872 |
completed | March 8, 2026, 3:45 p.m. |
| PD | Predicate disambiguation | batch_69ad96105a708190a9ec4838cbcb1207 |
completed | March 8, 2026, 3:30 p.m. |
| PDg | Predicate description generation | batch_69ad97f5d28c8190899d90204dc43428 |
completed | March 8, 2026, 3:38 p.m. |
Created at: March 8, 2026, 2:58 p.m.