Triple
T13430524
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Township of Pickle Lake |
E313594
|
entity |
| Predicate | hasLowPopulation |
P22797
|
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: [Township of Pickle Lake, hasLowPopulation, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLowPopulation Context triple: [Township of Pickle Lake, hasLowPopulation, true]
-
A.
hasLowPopulationDensity
Indicates that the number of individuals or entities per unit area in a given region is relatively small compared to typical or expected levels.
-
B.
hasVerySmallResidentPopulation
chosen
Indicates that the subject location has a resident population that is extremely small in size.
-
C.
populationLowPoint
Indicates that the population of an entity has reached its lowest recorded or observed level within a given time frame or context.
-
D.
hasNotablePopulation
Indicates that an entity is recognized for having a significant or noteworthy number of inhabitants or members.
-
E.
isLessPopulousThan
Indicates that one entity has a smaller population size than another entity.
- 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_69d806ad0c44819088833ae1ec9e9690 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbaed304ac8190a8021f749de8164c |
completed | April 12, 2026, 2:40 p.m. |
| PD | Predicate disambiguation | batch_69d9a03926188190ab3948d1f5d3941f |
completed | April 11, 2026, 1:13 a.m. |
Created at: April 9, 2026, 9:40 p.m.