Triple
T3869627
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | London City Airport |
E91950
|
entity |
| Predicate | hasRunwayCharacteristic |
P20697
|
FINISHED |
| Object | short runway |
—
|
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: short runway | Statement: [London City Airport, hasRunwayCharacteristic, short runway]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRunwayCharacteristic Context triple: [London City Airport, hasRunwayCharacteristic, short runway]
-
A.
hasRunwayType
Indicates that an airport or airfield has a runway of a specified type or surface classification.
-
B.
hasRunwayCount
Indicates the number of runways that a given entity (such as an airport) possesses.
-
C.
hasRunwayLengthCategory
chosen
Indicates that an airport or airfield is associated with a specific categorical range of runway lengths (e.g., short, medium, long).
-
D.
hasRunwayMarkings
Indicates that a runway possesses specific painted markings or symbols on its surface.
-
E.
hasRunwayNumber
Indicates that an airport or airfield runway is assigned a specific identifying number.
- 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_69aed9645f348190a9868e7cef56ab7e |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeec533828819080f52dae15fdbecd |
completed | March 9, 2026, 3:50 p.m. |
| PD | Predicate disambiguation | batch_69aee754dddc8190936e1f9c40a770db |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:20 p.m.