Triple
T31788971
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Port of Umeå |
E811408
|
entity |
| Predicate | hasCommercialTrafficTo |
P204076
|
FINISHED |
| Object | ports around the Baltic Sea |
—
|
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: ports around the Baltic Sea | Statement: [Port of Umeå, hasCommercialTrafficTo, ports around the Baltic Sea]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCommercialTrafficTo Context triple: [Port of Umeå, hasCommercialTrafficTo, ports around the Baltic Sea]
-
A.
hasTruckTraffic
Indicates that there is truck-related vehicular movement or flow occurring on or through a specified location or route.
-
B.
hasTrafficPattern
Indicates that there is a characteristic or recurring flow of traffic associated with an entity, such as its typical volume, direction, or timing of movement.
-
C.
hasPassengerTrafficFrom
Indicates that an entity receives or handles passenger traffic originating from another entity.
-
D.
hasTrafficFeature
Indicates that an entity possesses or is associated with a specific traffic-related characteristic, element, or infrastructure feature.
-
E.
hasTrafficMode
Indicates the mode or type of traffic associated with or applicable to an entity (e.g., pedestrian, vehicular, public transit).
- 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_69f348e60748819082dcaa7792659803 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_6a0317eaf4e88190a6f6419cb4ef7547 |
completed | May 12, 2026, 12:07 p.m. |
| PD | Predicate disambiguation | batch_6a03179da394819095e3d346c3785d74 |
completed | May 12, 2026, 12:05 p.m. |
| PDg | Predicate description generation | batch_6a0317ea3bc08190ac39ccd6b46da625 |
completed | May 12, 2026, 12:07 p.m. |
Created at: April 30, 2026, 11:38 p.m.