Triple
T3658668
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Airline Deregulation Act |
E77595
|
entity |
| Predicate | affectsIndustry |
P44194
|
FINISHED |
| Object | commercial aviation |
—
|
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: commercial aviation | Statement: [Airline Deregulation Act, affectsIndustry, commercial aviation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: affectsIndustry Context triple: [Airline Deregulation Act, affectsIndustry, commercial aviation]
-
A.
impactOnIndustry
chosen
Indicates the effect or influence that one entity, event, or action has on the state, performance, or development of an industry.
-
B.
sectorInfluence
Indicates the degree to which one sector affects, shapes, or exerts control over another sector or over outcomes within that sector.
-
C.
industryContext
Indicates the industry or sector within which an entity, activity, or relationship is situated or most relevant.
-
D.
affectedCompany
Indicates that a company is impacted or influenced by a particular event, action, or entity.
-
E.
impactOnBusiness
Indicates the effect or influence that one factor, event, or action has on a business’s performance, operations, or outcomes.
- 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_69ad85dfc4dc8190a441864202ab2a7a |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc3d48a2081908ac0f76d548a53ee |
completed | March 8, 2026, 6:45 p.m. |
| PD | Predicate disambiguation | batch_69adb847e9d881909dad2ffd0f3b6c15 |
completed | March 8, 2026, 5:56 p.m. |
Created at: March 8, 2026, 3:25 p.m.