Triple
T23901910
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Giovanni Battista Pastine Airport |
E601067
|
entity |
| Predicate | typicalTrafficType |
P27465
|
FINISHED |
| Object | low-cost flights |
—
|
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: low-cost flights | Statement: [Giovanni Battista Pastine Airport, typicalTrafficType, low-cost flights]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalTrafficType Context triple: [Giovanni Battista Pastine Airport, typicalTrafficType, low-cost flights]
-
A.
trafficType
Indicates the category or nature of traffic involved in a given interaction, flow, or connection (e.g., type of network, data, or transport traffic).
-
B.
coversTrafficType
Indicates that one entity includes, handles, or applies to a specified type or category of traffic.
-
C.
originalTrafficType
Indicates the initial category or source classification of traffic before any changes, redirects, or reattributions occur.
-
D.
majorTrafficType
chosen
Indicates the primary kind of traffic or flow that predominantly characterizes a given route, segment, or transportation context.
-
E.
traffics
Indicates engaging in the buying, selling, or illicit trading of someone or something, typically as part of an ongoing commercial or criminal operation.
- 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_69e295364a488190bcac702e9bb7f764 |
completed | April 17, 2026, 8:16 p.m. |
| NER | Named-entity recognition | batch_69f1cdde91d081908df44442a20e0fd2 |
completed | April 29, 2026, 9:22 a.m. |
| PD | Predicate disambiguation | batch_69f1614e24b48190a1c8fb5b7c75ee0f |
completed | April 29, 2026, 1:39 a.m. |
Created at: April 17, 2026, 8:26 p.m.