Triple
T35695825
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Båtsfjord Airport |
E1031435
|
entity |
| Predicate | hasLimitedPassengerCapacity |
P197866
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Båtsfjord Airport, hasLimitedPassengerCapacity, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLimitedPassengerCapacity Context triple: [Båtsfjord Airport, hasLimitedPassengerCapacity, true]
-
A.
maximumPassengerCapacity
Indicates the greatest number of passengers that an entity is designed or allowed to carry at one time.
-
B.
passengerCapacityCategory
Indicates the classification of an entity based on the number of passengers it is designed or allowed to carry.
-
C.
hasPassengerArea
Indicates that an object or vehicle includes a designated area intended for carrying passengers.
-
D.
hasCrewCapacity
Indicates that an entity is capable of accommodating a specified number of crew members.
-
E.
hasCrewCapacityType
Indicates that an entity is associated with a specific type or classification of crew capacity it can accommodate.
- 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_69f76e0c73ec819080ab60a9e2f5f1f6 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69feb342994081909481ec8ec5d44928 |
completed | May 9, 2026, 4:08 a.m. |
| PD | Predicate disambiguation | batch_69feb046e4e48190b96649aa28529cc9 |
completed | May 9, 2026, 3:55 a.m. |
| PDg | Predicate description generation | batch_69feb3419158819082f4666077535ca9 |
completed | May 9, 2026, 4:08 a.m. |
Created at: May 3, 2026, 4:05 p.m.