Triple
T10349732
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Federal Council of Germany |
E243847
|
entity |
| Predicate | hasPlenarySessionFrequency |
P1678
|
FINISHED |
| Object | about once a month |
—
|
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: about once a month | Statement: [Federal Council of Germany, hasPlenarySessionFrequency, about once a month]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPlenarySessionFrequency Context triple: [Federal Council of Germany, hasPlenarySessionFrequency, about once a month]
-
A.
hasPlenarySessions
Indicates that an event or organization includes formal plenary sessions as part of its activities or structure.
-
B.
hasPlenaryChamber
Indicates that an institution or governing body possesses a designated plenary chamber where all members can meet and deliberate together.
-
C.
hasLegislativeSessionCount
Indicates the number of legislative sessions associated with a given legislative body, term, or jurisdiction.
-
D.
legislativePeriodicity
chosen
Indicates how frequently a legislative body or process recurs or is scheduled to occur over time.
-
E.
hasPublicMeetings
Indicates that an entity organizes or holds meetings that are open and accessible to the general public.
- 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_69d381b22b8c8190aaed476be5f872a9 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e946cbb881909b88536d0107995d |
completed | April 7, 2026, 11:23 a.m. |
| PD | Predicate disambiguation | batch_69d4dfa657f481909cc5cc8fec00ad19 |
completed | April 7, 2026, 10:42 a.m. |
Created at: April 6, 2026, 11:57 a.m.