Triple
T893489
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dallas/Fort Worth International Airport |
E19290
|
entity |
| Predicate | hasLongHaulInternationalFlights |
P5534
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Dallas/Fort Worth International Airport, hasLongHaulInternationalFlights, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLongHaulInternationalFlights Context triple: [Dallas/Fort Worth International Airport, hasLongHaulInternationalFlights, yes]
-
A.
hasLongHaulFlights
chosen
Indicates that an entity (such as an airline, route, or airport) operates or is associated with long-distance flights typically covering intercontinental or extended-duration journeys.
-
B.
hasInternationalFlights
Indicates that an airport or airline operates flights connecting to destinations in other countries.
-
C.
hasDomesticFlights
Indicates that an airline or airport operates flights within the same country, connecting domestic destinations.
-
D.
hasTypeOfFlights
Indicates that an entity offers, includes, or is associated with specific categories or kinds of flights.
-
E.
travelsOn
Indicates that an entity moves or journeys using a particular route, path, or mode of transportation.
- 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.