Triple
T18652479
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | IEV |
E455975
|
entity |
| Predicate | associatedWithAirportRole |
P132895
|
FINISHED |
| Object | airport serving the capital city of Ukraine |
—
|
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: airport serving the capital city of Ukraine | Statement: [IEV, associatedWithAirportRole, airport serving the capital city of Ukraine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithAirportRole Context triple: [IEV, associatedWithAirportRole, airport serving the capital city of Ukraine]
-
A.
associatedWithAirportType
Indicates that an entity has a connection or linkage to a specific category or type of airport.
-
B.
associatedWithAirportCode
Indicates that one entity has a relationship or connection to an airport identified by a specific airport code.
-
C.
associatedWithAirportName
Indicates a relationship where an entity is linked or connected to a specific airport by its name.
-
D.
airportLineRole
Indicates the specific functional role or responsibility an entity has in relation to an airport transit line or route.
-
E.
associatedWithAirlineOperations
Indicates a relationship in which an entity is connected to, involved in, or relevant to the operations and activities of an airline.
- 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_69d8d38ea1e88190997e9b231190ba6f |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5501279d08190aeca36df89fee2b2 |
completed | April 19, 2026, 9:58 p.m. |
| PD | Predicate disambiguation | batch_69e478d85864819093cbad5ed9b54878 |
completed | April 19, 2026, 6:40 a.m. |
| PDg | Predicate description generation | batch_69e484121cd48190bf583b4c94636a30 |
completed | April 19, 2026, 7:28 a.m. |
Created at: April 10, 2026, 11:47 a.m.