Triple
T6489492
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Republic Airways |
E147997
|
entity |
| Predicate | passengerTypeServed |
P27830
|
FINISHED |
| Object | scheduled commercial passengers |
—
|
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: scheduled commercial passengers | Statement: [Republic Airways, passengerTypeServed, scheduled commercial passengers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: passengerTypeServed Context triple: [Republic Airways, passengerTypeServed, scheduled commercial passengers]
-
A.
hasPassengerServicesTo
Indicates that a transportation provider operates passenger services connecting one location or entity to another.
-
B.
servesPassengerTrafficType
chosen
Indicates that a transportation facility or service accommodates a specified type or category of passenger traffic.
-
C.
passengers
Indicates that one entity is traveling in or being transported by another entity, typically as a non-operating occupant.
-
D.
passengerCount
Indicates the number of passengers associated with a given entity, such as a vehicle or trip.
-
E.
passengerCapacityCategory
Indicates the classification of an entity based on the number of passengers it is designed or allowed to carry.
- 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_69c009088f3081909cd467b05919de30 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c06a9926fc81909db0f390e385e97d |
completed | March 22, 2026, 10:18 p.m. |
| PD | Predicate disambiguation | batch_69c06740bebc81909d9d6956baa2bcb9 |
completed | March 22, 2026, 10:03 p.m. |
Created at: March 22, 2026, 4:52 p.m.