Triple
T180795
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Manchester Airport |
E3870
|
entity |
| Predicate | hasRunway |
P105
|
FINISHED |
| Object | Runway 05L/23R |
E9457
|
NE 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: Runway 05L/23R | Statement: [Manchester Airport, hasRunway, Runway 05L/23R]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Runway 05L/23R Context triple: [Manchester Airport, hasRunway, Runway 05L/23R]
-
A.
Runway 05L/23R
chosen
Runway 05L/23R is one of the main parallel runways at Manchester Airport, used for handling a high volume of commercial air traffic.
-
B.
Runway 05R/23L
Runway 05R/23L is one of the main parallel runways at Manchester Airport in the United Kingdom, used for handling commercial air traffic.
-
C.
Runway 01L/19R
Runway 01L/19R is one of the primary parallel runways at San Francisco International Airport, used for both arrivals and departures in varying wind conditions.
-
D.
Runway 01R/19L
Runway 01R/19L is one of the primary parallel runways used for aircraft takeoffs and landings at San Francisco International Airport.
-
E.
Runway 15L/33R
Runway 15L/33R is one of the primary paved runways at Baltimore/Washington International Thurgood Marshall Airport, used for handling commercial air traffic in both directions.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69a25497e2f08190a040f8c6e1842643 |
completed | Feb. 28, 2026, 2:36 a.m. |
| NER | Named-entity recognition | batch_69a25901a9188190b8f510bec8c8e7f2 |
completed | Feb. 28, 2026, 2:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a2f0b71080819086362f6036b41162 |
completed | Feb. 28, 2026, 1:42 p.m. |
Created at: Feb. 28, 2026, 2:40 a.m.