Triple
T9573277
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CPA |
E230979
|
entity |
| Predicate | hasUnderlyingIndustry |
P13077
|
FINISHED |
| Object | airline industry |
—
|
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: airline industry | Statement: [CPA, hasUnderlyingIndustry, airline industry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasUnderlyingIndustry Context triple: [CPA, hasUnderlyingIndustry, airline industry]
-
A.
industryOfUnderlyingCompany
Indicates the industry sector in which the underlying company associated with this entity operates.
-
B.
hasUnderlyingCompanyBusinessModel
Indicates that one entity possesses or is based on a specific company business model that underlies its structure, operations, or value creation.
-
C.
hasUnderlyingIssuer
Indicates that one entity serves as the fundamental or primary issuer behind another entity, such as a financial instrument or structured product.
-
D.
hasParentCompanyIndustry
Indicates that an entity’s parent company operates in, or is associated with, a specified industry.
-
E.
hasPrincipalIndustry
chosen
Indicates that an entity’s main or primary industry of operation is the specified industry.
- 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_69ca848091c48190bc313d6620d09555 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd998d79cc8190b94e5953915a5fa4 |
completed | April 1, 2026, 10:17 p.m. |
| PD | Predicate disambiguation | batch_69ccd59b960c8190966a8870a2426bd5 |
completed | April 1, 2026, 8:21 a.m. |
Created at: March 30, 2026, 8:04 p.m.