Triple
T1453846
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Istanbul Airport |
E31352
|
entity |
| Predicate | designedCapacityPassengersPerYear |
P12993
|
FINISHED |
| Object | 200 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: 200 million | Statement: [Istanbul Airport, designedCapacityPassengersPerYear, 200 million]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: designedCapacityPassengersPerYear Context triple: [Istanbul Airport, designedCapacityPassengersPerYear, 200 million]
-
A.
hasAnnualPassengerTrafficOver
Indicates that the subject location or transport facility experiences an annual passenger volume exceeding a specified threshold.
-
B.
maximumPassengerCapacity
Indicates the greatest number of passengers that an entity is designed or allowed to carry at one time.
-
C.
annualCapacity
chosen
Indicates the maximum amount of output or throughput an entity can produce or handle within a one-year period.
-
D.
hasApproxAnnualPassengerUsageRank
Indicates the approximate position or ranking of an entity based on its annual passenger usage compared to similar entities.
-
E.
designedCargoCapacity
Indicates the maximum amount of cargo an object (such as a vehicle or container) was originally engineered or specified 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_69a499171a28819085b993a3ac78e363 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c57e82d48190a30a4512f39f5de0 |
completed | March 1, 2026, 11:02 p.m. |
| PD | Predicate disambiguation | batch_69a4c47cdbd0819092022344a2f4ad7b |
completed | March 1, 2026, 10:58 p.m. |
Created at: March 1, 2026, 8 p.m.