Triple
T36018869
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Runway 10-28 |
E1041927
|
entity |
| Predicate | hasRunwayNumberingStandard |
P55035
|
FINISHED |
| Object | ICAO runway designation system |
—
|
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: ICAO runway designation system | Statement: [Runway 10-28, hasRunwayNumberingStandard, ICAO runway designation system]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRunwayNumberingStandard Context triple: [Runway 10-28, hasRunwayNumberingStandard, ICAO runway designation system]
-
A.
usesRunwayNumberingConvention
chosen
Indicates that an airport or runway follows a specific standardized system for assigning runway identification numbers.
-
B.
hasRunwayNumberRange
Indicates that an entity (such as an airport or airfield) has runways whose identification numbers fall within a specified numeric range.
-
C.
hasRunwayNumber
Indicates that an airport or airfield runway is assigned a specific identifying number.
-
D.
hasRunwayMarkings
Indicates that a runway possesses specific painted markings or symbols on its surface.
-
E.
hasRunwayCount
Indicates the number of runways that a given entity (such as an airport) possesses.
- 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_69f76e2b981881908e4e160607fa82eb |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7bbf906d8819099020e548dd56bc9 |
completed | May 3, 2026, 9:19 p.m. |
| PD | Predicate disambiguation | batch_69f7b9a2dcf88190a7c9e109e41267be |
completed | May 3, 2026, 9:09 p.m. |
Created at: May 3, 2026, 4:07 p.m.