Triple
T7949327
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Milan Malpensa Airport |
E184573
|
entity |
| Predicate | runwayLengthMax |
P6291
|
FINISHED |
| Object | approximately 3920 m |
—
|
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: approximately 3920 m | Statement: [Milan Malpensa Airport, runwayLengthMax, approximately 3920 m]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: runwayLengthMax Context triple: [Milan Malpensa Airport, runwayLengthMax, approximately 3920 m]
-
A.
runwayLength
chosen
Indicates the length of a runway associated with an airport or airfield.
-
B.
runwayWidth
Indicates the measured width of a runway as a spatial dimension.
-
C.
hasLongestRunwayIn
Indicates that an entity possesses the runway of greatest length within a specified location or region.
-
D.
runwayRequirement
Indicates the minimum runway characteristics (such as length or surface conditions) needed for an aircraft or operation to take off or land safely.
-
E.
hasRunwayLengthCategory
Indicates that an airport or airfield is associated with a specific categorical range of runway lengths (e.g., short, medium, long).
- 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_69ca8292cba881908a64427b938dac47 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3b2d09a4819097aa49e29a5426ec |
completed | March 31, 2026, 3:10 a.m. |
| PD | Predicate disambiguation | batch_69cae9361bc48190886b7681e563d46b |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 5:10 p.m.