Triple
T893477
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dallas/Fort Worth International Airport |
E19290
|
entity |
| Predicate | rankingByPassengerTraffic |
P7989
|
FINISHED |
| Object | one of the busiest airports in the United States |
—
|
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: one of the busiest airports in the United States | Statement: [Dallas/Fort Worth International Airport, rankingByPassengerTraffic, one of the busiest airports in the United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankingByPassengerTraffic Context triple: [Dallas/Fort Worth International Airport, rankingByPassengerTraffic, one of the busiest airports in the United States]
-
A.
passengerTrafficRankingWorld
Indicates the relative position of an entity in a global ranking based on the volume of passenger traffic it handles.
-
B.
peakPassengerTrafficRank
Indicates the relative position of an entity in an ordered list based on the amount of passenger traffic it experiences at its peak.
-
C.
airportRank
Indicates the relative position or level assigned to an airport within a ranking or ordered list.
-
D.
passengerTrafficRankUS
chosen
Indicates the relative ranking of a location or facility within the United States based on the volume of passenger traffic it handles.
-
E.
passengerTraffic
Indicates the flow or volume of passengers moving through or using a particular transport service, route, or facility.
- 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_69a4939d37188190848be3d426ebc9ae |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ad212cd8819091eb1b7d606f5afd |
completed | March 1, 2026, 9:18 p.m. |
| PD | Predicate disambiguation | batch_69a4aa9372e88190b5a9db4afdc045c6 |
completed | March 1, 2026, 9:07 p.m. |
Created at: March 1, 2026, 7:39 p.m.