Triple
T3782464
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Montreal Metro |
E85450
|
entity |
| Predicate | terminusStation |
P15150
|
FINISHED |
| Object |
Montmorency
Montmorency is a Montreal Metro station in Laval that serves as the eastern terminus of the Orange Line and a key transit hub for the area.
|
E387554
|
NE FINISHED |
How this triple was built (4 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: Montmorency | Statement: [Montreal Metro, terminusStation, Montmorency]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Montmorency Context triple: [Montreal Metro, terminusStation, Montmorency]
-
A.
Montmorency
Montmorency is a commune in the northern suburbs of Paris, France, known for its historic town center and surrounding Val-d'Oise area.
-
B.
Butte-aux-Cailles
Butte-aux-Cailles is a picturesque, village-like neighborhood in Paris known for its cobbled streets, street art, and lively cafés.
-
C.
Acquigny
Acquigny is a commune in northern France known for its historic château, picturesque setting, and location at the confluence of the Eure and Iton rivers.
-
D.
Boncourt
Boncourt is a locality known for its historic Château de Boncourt, reflecting its cultural and architectural heritage.
-
E.
Éveux
Éveux is a small commune in eastern France’s Rhône department, known for hosting Le Corbusier’s modernist monastery, the Couvent Sainte-Marie de La Tourette.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Montmorency Triple: [Montreal Metro, terminusStation, Montmorency]
Generated description
Montmorency is a Montreal Metro station in Laval that serves as the eastern terminus of the Orange Line and a key transit hub for the area.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Montmorency Target entity description: Montmorency is a Montreal Metro station in Laval that serves as the eastern terminus of the Orange Line and a key transit hub for the area.
-
A.
Montmorency
Montmorency is a commune in the northern suburbs of Paris, France, known for its historic town center and surrounding Val-d'Oise area.
-
B.
Butte-aux-Cailles
Butte-aux-Cailles is a picturesque, village-like neighborhood in Paris known for its cobbled streets, street art, and lively cafés.
-
C.
Acquigny
Acquigny is a commune in northern France known for its historic château, picturesque setting, and location at the confluence of the Eure and Iton rivers.
-
D.
Boncourt
Boncourt is a locality known for its historic Château de Boncourt, reflecting its cultural and architectural heritage.
-
E.
Éveux
Éveux is a small commune in eastern France’s Rhône department, known for hosting Le Corbusier’s modernist monastery, the Couvent Sainte-Marie de La Tourette.
- F. None of above. chosen
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_69aed937fa8881908208ef3801060826 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aee3db11108190aa81ee8ed22709fe |
completed | March 9, 2026, 3:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4f04353a881908e612a10572eb8c5 |
completed | March 14, 2026, 5:21 a.m. |
| NEDg | Description generation | batch_69b4f0e77efc8190b4459d4559261a2f |
completed | March 14, 2026, 5:23 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b4f15cfde88190bcb2680b90f98111 |
completed | March 14, 2026, 5:25 a.m. |
Created at: March 9, 2026, 3:13 p.m.