Triple
T22455287
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Horseshoe Resort |
E555098
|
entity |
| Predicate | distanceFromBarrie |
P148265
|
FINISHED |
| Object | approximately 20 minutes by car |
—
|
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: approximately 20 minutes by car | Statement: [Horseshoe Resort, distanceFromBarrie, approximately 20 minutes by car]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromBarrie Context triple: [Horseshoe Resort, distanceFromBarrie, approximately 20 minutes by car]
-
A.
distanceFromToronto
Indicates the spatial distance between a given entity and the location of Toronto.
-
B.
distanceFromWaterloo
Indicates the spatial distance between a given location and Waterloo.
-
C.
distanceFromHamilton
Indicates the spatial distance between a given entity and the location identified as Hamilton.
-
D.
distanceToOttawa
Indicates the spatial distance between a given entity’s location and the city of Ottawa.
-
E.
distanceFromVictoria
Indicates the measured distance between a given entity or location and Victoria.
- 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_69e11e5113208190ab58c6b595f9d1d0 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15b4f19708190a50f29598fb1a204 |
completed | April 29, 2026, 1:13 a.m. |
| PD | Predicate disambiguation | batch_69e898ad961c819098fd1e46129bddcc |
completed | April 22, 2026, 9:45 a.m. |
| PDg | Predicate description generation | batch_69e8aa39e3388190b659d59948ebf3e6 |
completed | April 22, 2026, 11 a.m. |
Created at: April 16, 2026, 8:48 p.m.