Triple
T30751128
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kamloops Lake |
E782958
|
entity |
| Predicate | hasTransportationCorridorAlongShore |
P197448
|
FINISHED |
| Object | Canadian Pacific Railway main line |
—
|
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: Canadian Pacific Railway main line | Statement: [Kamloops Lake, hasTransportationCorridorAlongShore, Canadian Pacific Railway main line]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTransportationCorridorAlongShore Context triple: [Kamloops Lake, hasTransportationCorridorAlongShore, Canadian Pacific Railway main line]
-
A.
hasHighwayAlongShore
Indicates that a highway runs adjacent to or closely follows the shoreline of a body of water.
-
B.
extendsAlongCoastOf
Indicates that one entity stretches or runs parallel along the coastline of another entity.
-
C.
hasLongShoreline
Indicates that an entity possesses an extensive or unusually long shoreline relative to typical cases.
-
D.
connectsInlandCityToCoast
Indicates a relationship where a route, link, or infrastructure connects an inland city to a coastal area or city.
-
E.
hasShoreNear
Indicates that one entity is located close enough to another entity’s shore or coastline to be considered nearby.
- 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_69f224af8d8481908bea03890c5618be |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69fe920a437081908d5174e8cf7a53a6 |
completed | May 9, 2026, 1:46 a.m. |
| PD | Predicate disambiguation | batch_69fe919a9a6c8190acb4483f386e6db7 |
completed | May 9, 2026, 1:44 a.m. |
| PDg | Predicate description generation | batch_69fe9208ed708190980fb5061b22ae49 |
completed | May 9, 2026, 1:46 a.m. |
Created at: April 29, 2026, 8:39 p.m.