Triple
T17674277
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bologna Process |
E440603
|
entity |
| Predicate | frequencyOfMinisterialMeetings |
P128502
|
FINISHED |
| Object | every two to three years |
—
|
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: every two to three years | Statement: [Bologna Process, frequencyOfMinisterialMeetings, every two to three years]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: frequencyOfMinisterialMeetings Context triple: [Bologna Process, frequencyOfMinisterialMeetings, every two to three years]
-
A.
locationOfCabinetMeetings
Indicates the place where cabinet meetings are held.
-
B.
numberOfMinistries
Indicates the total count of ministries associated with or belonging to a given entity.
-
C.
hasNumberOfMinisters
Indicates the specific count of ministers associated with an entity, such as a government, cabinet, or organization.
-
D.
meetingsAre
Indicates that certain entities function as or are classified as meetings in relation to one another.
-
E.
convenesDuring
Indicates that one entity formally gathers or brings together another entity or group during a specified time period or event.
- 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_69d8b9e87e18819087104a44dc4dc5b1 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e46f6ba22081909e2099490c047378 |
completed | April 19, 2026, 6 a.m. |
| PD | Predicate disambiguation | batch_69e3cde007d8819090dd92eea9f022cc |
completed | April 18, 2026, 6:30 p.m. |
| PDg | Predicate description generation | batch_69e3cfaac2b881909e1140339eb1a0dd |
completed | April 18, 2026, 6:38 p.m. |
Created at: April 10, 2026, 10 a.m.