Triple
T23803389
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saint-Hyacinthe |
E589634
|
entity |
| Predicate | distanceToMontreal_km |
P89770
|
FINISHED |
| Object | about 60 |
—
|
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 60 | Statement: [Saint-Hyacinthe, distanceToMontreal_km, about 60]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToMontreal_km Context triple: [Saint-Hyacinthe, distanceToMontreal_km, about 60]
-
A.
distanceToMontreal
chosen
Indicates the spatial distance between a given entity’s location and the city of Montreal.
-
B.
distanceFromQuebecCity
Indicates the measured distance between a given place or object and Quebec City.
-
C.
distanceToGatineauByRoad_km
Indicates the length, in kilometers, of the road route needed to travel from an entity to Gatineau.
-
D.
distanceFromQuebecCityCentre
Indicates the measured spatial distance between a given location and the center of Quebec City.
-
E.
distanceToGrandePrairie_km
Indicates the distance, measured in kilometers, between a given entity’s location and Grande Prairie.
- 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_69e25d19fecc8190a5cf39bbb18d5d7f |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1c750048c8190899ff611df35b361 |
completed | April 29, 2026, 8:54 a.m. |
| PD | Predicate disambiguation | batch_69f155fe300481909bd617443228df65 |
completed | April 29, 2026, 12:51 a.m. |
Created at: April 17, 2026, 7:54 p.m.