Triple
T224867
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ottawa |
E4292
|
entity |
| Predicate | countrySubdivisionType |
P9832
|
FINISHED |
| Object | single-tier municipality |
—
|
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: single-tier municipality | Statement: [Ottawa, countrySubdivisionType, single-tier municipality]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: countrySubdivisionType Context triple: [Ottawa, countrySubdivisionType, single-tier municipality]
-
A.
countrySubdivision
Indicates that one geopolitical region is an administrative or territorial subdivision of a larger country.
-
B.
hasSubdivision
Indicates that one entity is divided into and contains another entity as one of its constituent parts or administrative units.
-
C.
regionType
Indicates the classification or category of a region, specifying what kind of region it is (e.g., administrative, geographic, or functional).
-
D.
countryType
Indicates the classification or category of a country based on a specified typology (e.g., political, economic, or geographic type).
-
E.
regionCodeType
Indicates the classification or format type used for a given region code within a coding or identification system.
- 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_69a2573508588190b522c2476d91acfe |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25dec53ac8190912f3d79576131fa |
completed | Feb. 28, 2026, 3:15 a.m. |
| PD | Predicate disambiguation | batch_69a25b5739dc8190bad8bfa330ce0499 |
completed | Feb. 28, 2026, 3:04 a.m. |
| PDg | Predicate description generation | batch_69a25dea93b48190863a3704b233aa03 |
completed | Feb. 28, 2026, 3:15 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.