Triple
T15779962
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Public Law 95-504 |
E382585
|
entity |
| Predicate | affectedIndustryStructure |
P44194
|
FINISHED |
| Object | increased number of competing airlines |
—
|
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: increased number of competing airlines | Statement: [Public Law 95-504, affectedIndustryStructure, increased number of competing airlines]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: affectedIndustryStructure Context triple: [Public Law 95-504, affectedIndustryStructure, increased number of competing airlines]
-
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.
affectedStructure
Indicates that one entity is the structure, component, or part that is impacted, altered, or influenced by another entity or event.
-
C.
affectedCompany
Indicates that a company is impacted or influenced by a particular event, action, or entity.
-
D.
supportedIndustry
Indicates that one entity provides backing, resources, or services to help sustain or advance a particular industry.
-
E.
operatorIndustry
Indicates that an operator (such as a company or organization) is engaged in or associated with a particular industry sector.
- 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_69d86da09a10819082fe9797b23e4664 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e053fea90081908e3fe4f91475bead |
completed | April 16, 2026, 3:14 a.m. |
| PD | Predicate disambiguation | batch_69e00537bd1c81908d6e832792fd934f |
completed | April 15, 2026, 9:37 p.m. |
Created at: April 10, 2026, 4:48 a.m.