Triple
T15732328
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Murmansk Airport |
E381373
|
entity |
| Predicate | hasDeIcingFacilities |
P103084
|
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: [Murmansk Airport, hasDeIcingFacilities, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDeIcingFacilities Context triple: [Murmansk Airport, hasDeIcingFacilities, yes]
-
A.
hasDeicingServices
chosen
Indicates that deicing services are available or provided for the referenced entity (such as an aircraft, vehicle, or facility).
-
B.
hasRunwayDeicingFacilities
Indicates that the subject location or facility is equipped with infrastructure or systems specifically for deicing aircraft runways.
-
C.
hasIceSurface
Indicates that an entity possesses or is characterized by a surface composed primarily of ice.
-
D.
hasSnowAndIce
Indicates that the subject is covered with or contains both snow and ice.
-
E.
hasIcePads
Indicates that an entity is equipped with or contains ice pads, typically used for cooling or temperature regulation.
- 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_69d86d9cdb648190bf3171be0bd7d872 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e04fd3614481908b2694b1d3550058 |
completed | April 16, 2026, 2:56 a.m. |
| PD | Predicate disambiguation | batch_69e00526759c819088b80d85138b8974 |
completed | April 15, 2026, 9:37 p.m. |
Created at: April 10, 2026, 4:46 a.m.