Triple
T12079365
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Morristown Municipal Airport |
E287636
|
entity |
| Predicate | hasDeicingServices |
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: [Morristown Municipal Airport, hasDeicingServices, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDeicingServices Context triple: [Morristown Municipal Airport, hasDeicingServices, yes]
-
A.
hasRunwayDeicingFacilities
Indicates that the subject location or facility is equipped with infrastructure or systems specifically for deicing aircraft runways.
-
B.
hasSnowAndIce
Indicates that the subject is covered with or contains both snow and ice.
-
C.
hasIceSurface
Indicates that an entity possesses or is characterized by a surface composed primarily of ice.
-
D.
hasSnowRemovalOperations
Indicates that an entity performs, manages, or is associated with activities related to removing snow from a specified area or infrastructure.
-
E.
hasGroundHandlingServices
Indicates that an entity provides or is associated with ground handling services for another entity, typically in an aviation or transportation context.
- F. None of above. chosen
Provenance (4 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_69d6ab4846e081908ee7bbd66a6d3459 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9100b4ca8819084845ca4c13e34ce |
completed | April 10, 2026, 2:58 p.m. |
| PD | Predicate disambiguation | batch_69d902bf4f508190842927e7e0642235 |
completed | April 10, 2026, 2:01 p.m. |
| PDg | Predicate description generation | batch_69d91006e14081909838412df082f794 |
completed | April 10, 2026, 2:58 p.m. |
Created at: April 8, 2026, 9:48 p.m.