Triple
T34976010
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Krakozhia |
E1008677
|
entity |
| Predicate | hasFictionalAirline |
P158093
|
FINISHED |
| Object | Krakozhian national airline |
—
|
NE NERFINISHED |
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: Krakozhian national airline | Statement: [Krakozhia, hasFictionalAirline, Krakozhian national airline]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalAirline Context triple: [Krakozhia, hasFictionalAirline, Krakozhian national airline]
-
A.
fictionalAirline
Indicates that an entity is an airline that exists only in fiction rather than in the real world.
-
B.
operatesFictionalFlight
Indicates that an entity runs or manages a flight service that exists only in a fictional or imaginary context.
-
C.
usesFictionalAircraftType
Indicates that an entity makes use of, operates, or features a type of aircraft that is fictional rather than real.
-
D.
hasFictionalCorporation
chosen
Indicates that an entity is associated with or includes a fictional corporation within its content, setting, or narrative.
-
E.
hasAirlines
Indicates that one entity (such as an airport, city, or country) is served by or associated with one or more airline operators.
- 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_69f76dc78a308190a1ac29ad4a9a4895 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69ffac35ac5481908b6bdfd5bbe8c76e |
completed | May 9, 2026, 9:50 p.m. |
| PD | Predicate disambiguation | batch_69ffabbfd2548190964c851496bbbaee |
completed | May 9, 2026, 9:48 p.m. |
Created at: May 3, 2026, 4:01 p.m.