Triple
T115932
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Invasion of Quebec (1775) |
E2337
|
entity |
| Predicate | approachRoute |
P6174
|
FINISHED |
| Object | Lake Champlain route |
—
|
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: Lake Champlain route | Statement: [Invasion of Quebec (1775), approachRoute, Lake Champlain route]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approachRoute Context triple: [Invasion of Quebec (1775), approachRoute, Lake Champlain route]
-
A.
hasApproachRoad
Indicates that one entity is connected to or accessed by another entity via an approach road leading to it.
-
B.
followsRouteOf
Indicates that one entity travels along the same path or route that another entity takes or has taken.
-
C.
routeNumber
Indicates the specific identifying number assigned to a route within a transportation or delivery network.
-
D.
hasEasiestRoute
Indicates that one entity provides or represents the simplest or least difficult route or path to reach another entity or destination.
-
E.
wentTo
Indicates that one entity traveled or moved from its original location to another specified place.
- 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_69a2506c5428819085c28a8884790e29 |
completed | Feb. 28, 2026, 2:18 a.m. |
| NER | Named-entity recognition | batch_69a257845c548190bfb49409988d1c57 |
completed | Feb. 28, 2026, 2:48 a.m. |
| PD | Predicate disambiguation | batch_69a256456d908190b52c937fe6c4343f |
completed | Feb. 28, 2026, 2:43 a.m. |
| PDg | Predicate description generation | batch_69a2578329d08190be82e004b8224d2b |
completed | Feb. 28, 2026, 2:48 a.m. |
Created at: Feb. 28, 2026, 2:24 a.m.