Triple
T26750233
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Autoroute 10 (Quebec) |
E674514
|
entity |
| Predicate | servesTouristRegion |
P71264
|
FINISHED |
| Object |
Mont-Orford area
The Mont-Orford area is a popular recreational and tourist region in Quebec known for its national park, ski resort, and outdoor activities year-round.
|
E1738884
|
NE 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: Mont-Orford area | Statement: [Autoroute 10 (Quebec), servesTouristRegion, Mont-Orford area]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Mont-Orford area Triple: [Autoroute 10 (Quebec), servesTouristRegion, Mont-Orford area]
Generated description
The Mont-Orford area is a popular recreational and tourist region in Quebec known for its national park, ski resort, and outdoor activities year-round.
Provenance (5 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_69eecda6e9dc81908452fab3ba17ed9b |
completed | April 27, 2026, 2:44 a.m. |
| NER | Named-entity recognition | batch_69f6188887048190bb119cec6af7a59c |
completed | May 2, 2026, 3:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a11feae8d54819099f2b4ee1ed38c07 |
completed | May 23, 2026, 7:23 p.m. |
| NEDg | Description generation | batch_6a11ff4907e88190aaad22b7390bc094 |
completed | May 23, 2026, 7:26 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a120048ef6c8190bf4467e0742a0421 |
completed | May 23, 2026, 7:30 p.m. |
Created at: April 27, 2026, 3:53 a.m.