Triple
T36443575
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lac-Beauport |
E897802
|
entity |
| Predicate | distanceToQuebecCity |
P131459
|
FINISHED |
| Object | approximately 25 kilometres |
—
|
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 25 kilometres | Statement: [Lac-Beauport, distanceToQuebecCity, approximately 25 kilometres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToQuebecCity Context triple: [Lac-Beauport, distanceToQuebecCity, approximately 25 kilometres]
-
A.
distanceFromQuebecCity
chosen
Indicates the measured distance between a given place or object and Quebec City.
-
B.
distanceToMontreal
Indicates the spatial distance between a given entity’s location and the city of Montreal.
-
C.
distanceFromQuebecCityCentre
Indicates the measured spatial distance between a given location and the center of Quebec City.
-
D.
distanceToOttawa
Indicates the spatial distance between a given entity’s location and the city of Ottawa.
-
E.
distanceFromGatineau
Indicates the spatial distance between a given entity or location and the city of Gatineau.
- 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_69f76e5720b481908f8177ac24a7560b |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c8e2c648190a65fc9c7872861af |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0a54cc8190868c1bfa1590d1a6 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:10 p.m.