Triple
T1702506
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brussels Airport |
E36797
|
entity |
| Predicate | passengerTrafficRankInBelgium |
P32426
|
FINISHED |
| Object | busiest airport in Belgium |
—
|
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: busiest airport in Belgium | Statement: [Brussels Airport, passengerTrafficRankInBelgium, busiest airport in Belgium]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: passengerTrafficRankInBelgium Context triple: [Brussels Airport, passengerTrafficRankInBelgium, busiest airport in Belgium]
-
A.
passengerTrafficRankInEurope
Indicates the relative position of an entity in Europe based on the volume of passenger traffic it handles.
-
B.
cargoTrafficRankInEurope
Indicates the relative position of an entity in terms of cargo traffic volume compared to other entities within Europe.
-
C.
passengerTrafficRankingWorld
Indicates the relative position of an entity in a global ranking based on the volume of passenger traffic it handles.
-
D.
cargoTrafficRankInFrance
Indicates the ranking position of an entity based on the volume of cargo traffic it handles within France.
-
E.
passengerTraffic
Indicates the flow or volume of passengers moving through or using a particular transport service, route, or facility.
- F. None of above. chosen
Provenance (4 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_69a88617439c819094ffb5d16a0f6307 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69ab75ad24408190814069e6e3ef9e59 |
completed | March 7, 2026, 12:47 a.m. |
| PD | Predicate disambiguation | batch_69aa61bad17c8190861b92cfb423f68f |
completed | March 6, 2026, 5:10 a.m. |
| PDg | Predicate description generation | batch_69ab75ac1408819086b22b3cd0672a79 |
completed | March 7, 2026, 12:47 a.m. |
Created at: March 4, 2026, 7:30 p.m.