Triple
T4715892
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | canton of Bern |
E104636
|
entity |
| Predicate | hasAreaRankInSwitzerland |
P50319
|
FINISHED |
| Object | second largest canton by area |
—
|
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: second largest canton by area | Statement: [canton of Bern, hasAreaRankInSwitzerland, second largest canton by area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAreaRankInSwitzerland Context triple: [canton of Bern, hasAreaRankInSwitzerland, second largest canton by area]
-
A.
rankInSwitzerlandByArea
chosen
Indicates the position of an entity in an ordered list of areas specifically within Switzerland, based on its size relative to others.
-
B.
populationRankInCanton
Indicates the relative position of an entity in terms of population size compared to other entities within the same canton.
-
C.
rankingByLengthInSwitzerland
Indicates that entities are ordered or evaluated based on their length within the context of Switzerland.
-
D.
largestCantonByPopulation
Indicates that the subject is the canton with the highest population among a specified set or within a given region.
-
E.
chartPositionSwitzerland
Indicates the position or ranking that something holds on a music or sales chart specifically in Switzerland.
- 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_69bd43ec4a348190bc41afae43375e71 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd680beb508190b3d74e20e1c64405 |
completed | March 20, 2026, 3:30 p.m. |
| PD | Predicate disambiguation | batch_69bd621ddcd88190903288566f5e5dab |
completed | March 20, 2026, 3:05 p.m. |
Created at: March 20, 2026, 1:18 p.m.