Triple
T26278223
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Chinita International Airport |
E660631
|
entity |
| Predicate | operatesTypeOfTraffic |
P107210
|
FINISHED |
| Object | civil |
—
|
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: civil | Statement: [La Chinita International Airport, operatesTypeOfTraffic, civil]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: operatesTypeOfTraffic Context triple: [La Chinita International Airport, operatesTypeOfTraffic, civil]
-
A.
coversTrafficType
Indicates that one entity includes, handles, or applies to a specified type or category of traffic.
-
B.
managesTrafficFor
Indicates that one entity is responsible for directing, controlling, or regulating the flow of traffic for another entity or area.
-
C.
majorTrafficType
Indicates the primary kind of traffic or flow that predominantly characterizes a given route, segment, or transportation context.
-
D.
hasTrafficMode
chosen
Indicates the mode or type of traffic associated with or applicable to an entity (e.g., pedestrian, vehicular, public transit).
-
E.
hasCargoTrafficType
Indicates that an entity is associated with a specific type or category of cargo traffic it handles or supports.
- 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_69ee812960d081909cff6085cc9fa3a6 |
completed | April 26, 2026, 9:18 p.m. |
| NER | Named-entity recognition | batch_69fcf36d2894819089b7db8e91b63c9d |
completed | May 7, 2026, 8:17 p.m. |
| PD | Predicate disambiguation | batch_69fcf25c0a108190bfa823474098640b |
completed | May 7, 2026, 8:13 p.m. |
Created at: April 26, 2026, 9:57 p.m.