Triple
T36240941
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Long Thanh International Airport |
E891514
|
entity |
| Predicate | plannedCapacityPhase1PassengersPerYear |
P12993
|
FINISHED |
| Object | 25 million |
—
|
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: 25 million | Statement: [Long Thanh International Airport, plannedCapacityPhase1PassengersPerYear, 25 million]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: plannedCapacityPhase1PassengersPerYear Context triple: [Long Thanh International Airport, plannedCapacityPhase1PassengersPerYear, 25 million]
-
A.
visitorCapacityPlanned
Indicates the planned or intended maximum number of visitors that a place or event is designed to accommodate.
-
B.
hasAnnualPassengerTrafficOver
Indicates that the subject location or transport facility experiences an annual passenger volume exceeding a specified threshold.
-
C.
hasNumberOfSeatsAtPeak
Indicates the maximum number of seats available or occupied at the peak usage or capacity of something.
-
D.
annualCapacity
chosen
Indicates the maximum amount of output or throughput an entity can produce or handle within a one-year period.
-
E.
hasApproxAnnualPassengerUsageRank
Indicates the approximate position or ranking of an entity based on its annual passenger usage compared to similar entities.
- 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_69f76e44993481908fa75e4c48d0aab3 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fd3d46d1f48190a1b20dd063224b7d |
completed | May 8, 2026, 1:32 a.m. |
| PD | Predicate disambiguation | batch_69fd3ae1510c81908fe1280efc17feee |
completed | May 8, 2026, 1:22 a.m. |
Created at: May 3, 2026, 4:09 p.m.