Triple
T28051116
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gunn effect |
E708821
|
entity |
| Predicate | requiresBias |
P149342
|
FINISHED |
| Object | dc bias above threshold field |
—
|
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: dc bias above threshold field | Statement: [Gunn effect, requiresBias, dc bias above threshold field]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: requiresBias Context triple: [Gunn effect, requiresBias, dc bias above threshold field]
-
A.
requiresBiasType
Indicates that one entity must be associated with or specify a particular type of bias as a prerequisite or condition for the relationship or action.
-
B.
requiresBiasVoltage
chosen
Indicates that one entity must have a specific bias voltage applied in order for the other entity or process to operate correctly or as intended.
-
C.
bias
Indicates a systematic preference or prejudice in favor of or against an entity, affecting how it is treated, evaluated, or represented relative to others.
-
D.
hadMembershipBias
Indicates that a membership or affiliation influenced the treatment, decision, or outcome in a biased or preferential way.
-
E.
supportsRegionBiasing
Indicates that the subject is capable of applying or honoring region-specific preferences or priorities in its behavior or processing.
- 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_69ef9b6df9f48190bbb971d02cbe1b65 |
completed | April 27, 2026, 5:22 p.m. |
| NER | Named-entity recognition | batch_69fcd867f36081908c88c55a6a1404c1 |
completed | May 7, 2026, 6:22 p.m. |
| PD | Predicate disambiguation | batch_69fcd1f47b188190b4cf4b4c748d9d03 |
completed | May 7, 2026, 5:55 p.m. |
Created at: April 27, 2026, 8:33 p.m.