Triple
T15980396
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Côte-Vertu |
E387555
|
entity |
| Predicate | servesBorough |
P82
|
FINISHED |
| Object | Saint-Laurent |
E110596
|
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: Saint-Laurent | Statement: [Côte-Vertu, servesBorough, Saint-Laurent]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saint-Laurent Context triple: [Côte-Vertu, servesBorough, Saint-Laurent]
-
A.
Saint-Laurent
chosen
Saint-Laurent is a borough of Montreal known as a major residential and industrial hub on the Island of Montreal in Quebec, Canada.
-
B.
Couture-Saint-Germain
Couture-Saint-Germain is a village in Walloon Brabant, Belgium, known as one of the constituent districts of the municipality of Lasne.
-
C.
Boucicaut
Boucicaut is a station on the Paris Métro serving the 15th arrondissement of Paris.
-
D.
Bettencourt
Bettencourt is a prominent French surname most famously associated with Liliane Bettencourt, the L'Oréal heiress and once one of the world's wealthiest women.
-
E.
Goutte d'Or
Goutte d'Or is a vibrant, historically working-class neighborhood in Paris known for its diverse immigrant communities, bustling markets, and rich North and West African cultural influences.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d86da94ccc819083d187f5dc6a123e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e157542cd88190832e7ae79bd38ffc |
completed | April 16, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffcf1abad48190a42510605c30d0b3 |
completed | May 10, 2026, 12:19 a.m. |
Created at: April 10, 2026, 4:54 a.m.