Triple
T6062565
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nowy Targ Airport |
E135067
|
entity |
| Predicate | hasNoRegular |
P1817
|
FINISHED |
| Object | scheduled passenger services |
—
|
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: scheduled passenger services | Statement: [Nowy Targ Airport, hasNoRegular, scheduled passenger services]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNoRegular Context triple: [Nowy Targ Airport, hasNoRegular, scheduled passenger services]
-
A.
hasRegularity
Indicates that one entity exhibits a consistent, recurring pattern or uniform behavior with respect to another entity or over time.
-
B.
hasNoConventionalSubject
Indicates that an action or event occurs without a typical, explicit grammatical subject performing it.
-
C.
doesNotHave
chosen
Indicates that one entity lacks, is missing, or is not in possession of another entity or attribute.
-
D.
hasNonExample
Indicates that something is associated with an instance that explicitly does not satisfy or illustrate a given concept, rule, or category.
-
E.
hadNo
Indicates that one entity completely lacked or did not possess another entity, attribute, or relationship.
- 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_69c00878d06881909ee78e88913bf890 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c0572100e8819084c3174921a2d527 |
completed | March 22, 2026, 8:54 p.m. |
| PD | Predicate disambiguation | batch_69c049f031408190b08b2766237c5dd0 |
completed | March 22, 2026, 7:58 p.m. |
Created at: March 22, 2026, 4:10 p.m.