Triple
T6630193
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tucson International Airport |
E149902
|
entity |
| Predicate | hasRentalCarFacilities |
P6090
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Tucson International Airport, hasRentalCarFacilities, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRentalCarFacilities Context triple: [Tucson International Airport, hasRentalCarFacilities, yes]
-
A.
hasRentalCarCenter
chosen
Indicates that a location or facility includes or is associated with a rental car center where vehicles can be rented.
-
B.
hasFacilities
Indicates that an entity possesses, provides, or is equipped with certain facilities or physical resources.
-
C.
hasParkFeature
Indicates that a park includes, contains, or is associated with a specific feature or amenity.
-
D.
hasMaintenanceFacilities
Indicates that one entity provides or contains facilities where the other entity can be serviced, repaired, or maintained.
-
E.
hasPassengerServicesTo
Indicates that a transportation provider operates passenger services connecting one location or entity to another.
- 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_69c687ee50048190aa151765bef16193 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6c308a08881908501c862b3029321 |
completed | March 27, 2026, 5:48 p.m. |
| PD | Predicate disambiguation | batch_69c6ad024860819084b9b535b136ede6 |
completed | March 27, 2026, 4:14 p.m. |
Created at: March 27, 2026, 1:59 p.m.