Triple
T34873549
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vancouver–Kamloops |
E1005816
|
entity |
| Predicate | railDistanceApproxKm |
P65643
|
FINISHED |
| Object | about 350 |
—
|
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: about 350 | Statement: [Vancouver–Kamloops, railDistanceApproxKm, about 350]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: railDistanceApproxKm Context triple: [Vancouver–Kamloops, railDistanceApproxKm, about 350]
-
A.
approximateDistanceKm
Indicates the estimated distance between two entities measured in kilometers, typically with some degree of inaccuracy or approximation.
-
B.
tourDistanceApproxKm
Indicates an approximate total distance, measured in kilometers, covered during a tour or journey.
-
C.
flightDistance
Indicates the measured distance covered by a flight between its origin and destination.
-
D.
distanceToBudapest_km
Indicates the physical distance, measured in kilometers, between a given location and Budapest.
-
E.
distanceToByRail
chosen
Indicates the distance between two locations when traveling via railway routes.
- 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_69f76dbde1c08190a24e7f9beb564c8d |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f782f4f10081908f97f6d0d2dbeec7 |
completed | May 3, 2026, 5:16 p.m. |
| PD | Predicate disambiguation | batch_69f780ff71cc8190a67e71076fbad81a |
completed | May 3, 2026, 5:08 p.m. |
Created at: May 3, 2026, 4 p.m.