Triple
T26332328
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lorraine (Quebec) |
E662419
|
entity |
| Predicate | distanceFromMontrealDowntown |
P89770
|
FINISHED |
| Object | approximately 25 km |
—
|
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 km | Statement: [Lorraine (Quebec), distanceFromMontrealDowntown, approximately 25 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromMontrealDowntown Context triple: [Lorraine (Quebec), distanceFromMontrealDowntown, approximately 25 km]
-
A.
distanceFromQuebecCityCentre
Indicates the measured spatial distance between a given location and the center of Quebec City.
-
B.
distanceToMontreal
chosen
Indicates the spatial distance between a given entity’s location and the city of Montreal.
-
C.
distanceFromQuebecCity
Indicates the measured distance between a given place or object and Quebec City.
-
D.
distanceFromLaval
Indicates the spatial distance between an entity and the location of Laval.
-
E.
distanceToGatineauByRoad_km
Indicates the length, in kilometers, of the road route needed to travel from an entity to 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_69ee812f32748190871d970c4e2a8ddf |
completed | April 26, 2026, 9:18 p.m. |
| NER | Named-entity recognition | batch_69fe00dad1708190b6522476bebb43af |
completed | May 8, 2026, 3:27 p.m. |
| PD | Predicate disambiguation | batch_69fdfc3717f48190bb50ac2919c8ef95 |
completed | May 8, 2026, 3:07 p.m. |
Created at: April 26, 2026, 10:34 p.m.