Triple
T17795965
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Berlusconi II Cabinet |
E444292
|
entity |
| Predicate | numberOfUndersecretaries |
P15820
|
FINISHED |
| Object | 60 |
—
|
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: 60 | Statement: [Berlusconi II Cabinet, numberOfUndersecretaries, 60]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfUndersecretaries Context triple: [Berlusconi II Cabinet, numberOfUndersecretaries, 60]
-
A.
numberOfCabinetMembers
Indicates the total count of cabinet members associated with a given government, administration, or leader.
-
B.
numberOfStateSecretaries
chosen
Indicates the quantity of state secretaries associated with a given entity or context.
-
C.
numberOfMinistersLimit
Indicates a constraint specifying the maximum allowable number of ministers in a given context or governing body.
-
D.
hasNumberOfMinisters
Indicates the specific count of ministers associated with an entity, such as a government, cabinet, or organization.
-
E.
numberOfMinistries
Indicates the total count of ministries associated with or belonging to a given 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_69d8b9efe370819095cd219b143ae727 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e487fafc2c8190b28e791267c47e3c |
completed | April 19, 2026, 7:44 a.m. |
| PD | Predicate disambiguation | batch_69e3d8de28688190844b65acf6af54e6 |
completed | April 18, 2026, 7:17 p.m. |
Created at: April 10, 2026, 10:13 a.m.