Triple
T954742
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Swiss Federal Assembly |
E20600
|
entity |
| Predicate | sessionsPerYear |
P6891
|
FINISHED |
| Object | 4 regular sessions |
—
|
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: 4 regular sessions | Statement: [Swiss Federal Assembly, sessionsPerYear, 4 regular sessions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sessionsPerYear Context triple: [Swiss Federal Assembly, sessionsPerYear, 4 regular sessions]
-
A.
sessionCount
chosen
Indicates the number of distinct sessions associated with an entity or interaction context.
-
B.
typicalNumberOfMeetingsPerSeason
Indicates the usual or average count of meetings that occur within a single season.
-
C.
typicalNumberOfRecipientsPerYear
Indicates the usual or average count of recipients involved in or affected by something within a one-year period.
-
D.
isUsedAllYear
Indicates that something is utilized or remains in active use throughout the entire year, without being limited to a particular season or period.
-
E.
numberOfEvents
Indicates the quantity or count of events associated with a given entity or context.
- 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_69a493b21f2881908132dcf45dcd2f36 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b3da8d508190b56b29d7f235d2c4 |
completed | March 1, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69a4b2a18ecc8190883f6206fe3b0fb6 |
completed | March 1, 2026, 9:41 p.m. |
Created at: March 1, 2026, 7:40 p.m.