Triple
T35980309
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lakeshore |
E1040543
|
entity |
| Predicate | hasSignificantFrancophonePopulation |
P196063
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Lakeshore, hasSignificantFrancophonePopulation, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSignificantFrancophonePopulation Context triple: [Lakeshore, hasSignificantFrancophonePopulation, true]
-
A.
isFrancophoneCounterpartOf
Indicates that one entity serves as the French-speaking or French-language equivalent or counterpart of another entity.
-
B.
isFrancophoneParty
Indicates that a political party primarily uses French or represents French-speaking communities.
-
C.
primaryFrenchDestination
Indicates that one entity is the main or most significant travel destination in France for another entity.
-
D.
isInFrancophoneRegion
Indicates that an entity is located within a region where French is predominantly spoken or officially used.
-
E.
hasFrenchSector
Indicates that an entity includes, controls, or is associated with a sector or area designated as French.
- 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_69f76e28293c8190ae3f4e2208b87117 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fe031bc6208190860099aef72d8dcb |
completed | May 8, 2026, 3:36 p.m. |
| PD | Predicate disambiguation | batch_69fe014c8b388190b5d4e0cb95ee2be5 |
completed | May 8, 2026, 3:29 p.m. |
| PDg | Predicate description generation | batch_69fe031af3248190816da6829aef7bab |
completed | May 8, 2026, 3:36 p.m. |
Created at: May 3, 2026, 4:07 p.m.