Triple
T1345200
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Montreal Impact |
E28553
|
entity |
| Predicate | cityLanguageContext |
P18209
|
FINISHED |
| Object | French-speaking majority city |
—
|
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: French-speaking majority city | Statement: [Montreal Impact, cityLanguageContext, French-speaking majority city]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cityLanguageContext Context triple: [Montreal Impact, cityLanguageContext, French-speaking majority city]
-
A.
regionLanguage
Indicates that a particular language is used or officially recognized within a specific geographic region.
-
B.
city2
Indicates a relationship where one entity is identified as a city associated with, located in, or otherwise linked to another entity.
-
C.
cityStatusContext
Indicates the contextual status or role that a city holds within a broader administrative, political, or situational framework.
-
D.
languageUse
chosen
Indicates the language or languages an entity uses for communication, expression, or interaction.
-
E.
hasLanguageOfToponym
Indicates that a place name (toponym) is expressed in or associated with a particular language.
- 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_69a49854eb3481908c7d56b2e449a290 |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c23d696c8190bb688274280cb680 |
completed | March 1, 2026, 10:48 p.m. |
| PD | Predicate disambiguation | batch_69a4bef5857c81909ae984feb85a26ca |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:56 p.m.