Triple
T501666
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | IEEE Control Systems Award |
E10414
|
entity |
| Predicate | areaOfImpact |
P9961
|
FINISHED |
| Object | automatic control |
—
|
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: automatic control | Statement: [IEEE Control Systems Award, areaOfImpact, automatic control]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: areaOfImpact Context triple: [IEEE Control Systems Award, areaOfImpact, automatic control]
-
A.
affectedArea
Indicates the specific region or extent over which an event, condition, or influence has an impact.
-
B.
socialImpact
Indicates the extent to which an action, entity, or relationship affects society or communities, whether positively or negatively.
-
C.
affectedAgency
Indicates that one entity has an effect on, or causes a change in, the agency or capacity for action of another entity.
-
D.
scopeOfContribution
chosen
Indicates the specific area, domain, or extent within which an entity’s contribution or involvement applies.
-
E.
affectedCity
Indicates that a particular city is impacted or influenced by a specified event, action, or condition.
- 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_69a2e848adf881908e5e04f7af030093 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f131e2148190afd43402f505c73e |
completed | Feb. 28, 2026, 1:44 p.m. |
| PD | Predicate disambiguation | batch_69a2edfbb7e0819092cf29c2c68fe8fb |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.