Triple
T38511019
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bowron Lake Canoe Circuit |
E921904
|
entity |
| Predicate | usesWaterBodies |
P43276
|
FINISHED |
| Object | lakes |
—
|
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: lakes | Statement: [Bowron Lake Canoe Circuit, usesWaterBodies, lakes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesWaterBodies Context triple: [Bowron Lake Canoe Circuit, usesWaterBodies, lakes]
-
A.
appliesToWaterBody
Indicates that something (such as a rule, condition, property, or effect) is relevant or applicable specifically to a particular water body.
-
B.
focusesOnWaterBodies
chosen
Indicates a relationship where the subject’s attention, activity, or primary concern is directed toward water bodies such as lakes, rivers, or oceans.
-
C.
waterbodyUsedFor
Indicates that a particular water body is utilized for a specific purpose, activity, or function.
-
D.
hasWaterFeatures
Indicates that an entity includes or is associated with water-related elements such as fountains, ponds, streams, or similar features.
-
E.
involvesBodyOfWater
Indicates that the relationship or event includes, affects, or takes place in connection with a body of water such as a sea, lake, river, or ocean.
- 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_69f76ea3c5448190aa7002fc1ba3f874 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fcd313e61c8190b174b331365b803f |
completed | May 7, 2026, 5:59 p.m. |
| PD | Predicate disambiguation | batch_69fcd1f6b2e08190bf0300ae7c9ae67a |
completed | May 7, 2026, 5:55 p.m. |
Created at: May 3, 2026, 4:32 p.m.