Triple
T30209819
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chester Hawarden Airport |
E768037
|
entity |
| Predicate | hasApronUse |
P184605
|
FINISHED |
| Object | Airbus aircraft movements |
—
|
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: Airbus aircraft movements | Statement: [Chester Hawarden Airport, hasApronUse, Airbus aircraft movements]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApronUse Context triple: [Chester Hawarden Airport, hasApronUse, Airbus aircraft movements]
-
A.
hasApron
Indicates that one entity possesses or is wearing an apron in relation to another context or entity.
-
B.
hasApronType
Indicates that an entity is associated with or characterized by a specific type or category of apron.
-
C.
hasMilitaryApron
Indicates that a location or facility includes a designated apron area specifically used for military aircraft operations.
-
D.
isWornBefore
Indicates that one item of clothing or accessory is put on earlier in time than another item.
-
E.
usesDressing
Indicates that one entity applies or employs a particular dressing (such as a sauce, covering, or treatment) in relation to another entity or 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_69f2247eb0848190b4032f302d39c0d9 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f7b365288c8190bcb11fcfba028737 |
completed | May 3, 2026, 8:43 p.m. |
| PD | Predicate disambiguation | batch_69f7b1b8a9fc8190a1279e67a2d12707 |
completed | May 3, 2026, 8:36 p.m. |
| PDg | Predicate description generation | batch_69f7b2f2b9ac8190aa05b8a1aa18ec2d |
completed | May 3, 2026, 8:41 p.m. |
Created at: April 29, 2026, 7:32 p.m.