Triple
T21119083
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Airport and Airway Trust Fund |
E520376
|
entity |
| Predicate | hasSpendingCategory |
P24220
|
FINISHED |
| Object | capital investment in airports |
—
|
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: capital investment in airports | Statement: [Airport and Airway Trust Fund, hasSpendingCategory, capital investment in airports]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSpendingCategory Context triple: [Airport and Airway Trust Fund, hasSpendingCategory, capital investment in airports]
-
A.
supportsSpendingCategory
chosen
Indicates that one entity allows, enables, or is compatible with making expenditures in a specified spending category.
-
B.
hasCategories
Indicates that an entity is associated with one or more categories that classify or group it.
-
C.
includesCreditCategory
Indicates that one entity contains or encompasses a specific credit-related category within its defined set or structure.
-
D.
payCategory
Indicates the classification of a payment or compensation into a specific category (such as type, purpose, or pay band) within a payment or payroll context.
-
E.
hasCategoryOn
Indicates that something is assigned to or associated with a specific category within a given context or scope.
- 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_69e0b50a623881909c0bbaf4f2c055e7 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7223176c48190bfbaea41c2209a15 |
completed | April 21, 2026, 7:07 a.m. |
| PD | Predicate disambiguation | batch_69e5dbff56848190a03b350a9305c612 |
completed | April 20, 2026, 7:55 a.m. |
Created at: April 16, 2026, 2:55 p.m.