Triple
T4433510
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frankfurt Airport |
E95589
|
entity |
| Predicate | hasCargoTrafficRank |
P54984
|
FINISHED |
| Object | among leading cargo airports in Europe |
—
|
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: among leading cargo airports in Europe | Statement: [Frankfurt Airport, hasCargoTrafficRank, among leading cargo airports in Europe]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCargoTrafficRank Context triple: [Frankfurt Airport, hasCargoTrafficRank, among leading cargo airports in Europe]
-
A.
hasPassengerTrafficRank
Indicates the relative position or ranking of an entity based on the volume of passenger traffic it handles compared to others.
-
B.
cargoTrafficRank
chosen
Indicates the relative position of an entity in an ordered list based on the volume or intensity of its cargo traffic.
-
C.
hasCargoTrafficType
Indicates that an entity is associated with a specific type or category of cargo traffic it handles or supports.
-
D.
peakFreightTrafficRank
Indicates the relative ranking position of an entity based on the highest level of freight traffic it experiences or handles compared to others.
-
E.
passengerTrafficRankingWorld
Indicates the relative position of an entity in a global ranking based on the volume of passenger traffic it handles.
- 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_69b3453ea2b48190a26f154b3b8fece5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35587bc048190aee8e0ed94b6e064 |
completed | March 13, 2026, 12:08 a.m. |
| PD | Predicate disambiguation | batch_69b34f6078cc8190831b89f404198cc5 |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:31 p.m.