Triple
T1034237
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Basel-Stadt |
E22322
|
entity |
| Predicate | isSmallestCantonByArea |
P14659
|
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: [Basel-Stadt, isSmallestCantonByArea, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isSmallestCantonByArea Context triple: [Basel-Stadt, isSmallestCantonByArea, true]
-
A.
isSmallestByAreaIn
chosen
Indicates that an entity has the smallest area among all comparable entities within a specified set, group, or context.
-
B.
isSmallestCountryIn
Indicates that a country is the smallest (by a specified measure, typically area) among all countries within a given region or set.
-
C.
largestStatelessNation
Indicates that the subject is the largest nation (by population, territory, or other measure) that does not possess an independent sovereign state of its own.
-
D.
hasCanton
Indicates that an entity is administratively divided into, or associated with, a specific canton.
-
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_69a493d848848190aed4011b34b2e8d3 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b8d669448190955507e2e4975b9f |
completed | March 1, 2026, 10:08 p.m. |
| PD | Predicate disambiguation | batch_69a4b728ad3481909cf1430349cb9bba |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:41 p.m.