Triple
T37304687
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Judge of the Constitutional Court of Italy |
E926045
|
entity |
| Predicate | numberOfPresidentialAppointees |
P205828
|
FINISHED |
| Object | 5 |
—
|
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: 5 | Statement: [Judge of the Constitutional Court of Italy, numberOfPresidentialAppointees, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfPresidentialAppointees Context triple: [Judge of the Constitutional Court of Italy, numberOfPresidentialAppointees, 5]
-
A.
numberOfCabinetMembers
Indicates the total count of cabinet members associated with a given government, administration, or leader.
-
B.
numberOfAssociateJustices
Indicates the total count of associate justices associated with a given judicial body or court.
-
C.
appointedOfficeHolders
Indicates that an authority or body has formally selected and installed specific individuals into particular offices or positions.
-
D.
numberOfSupremeCourtJustices
Indicates the total count of individuals serving as justices on a specified Supreme Court.
-
E.
numberAppointedBySenate
Indicates the number of individuals who were appointed to a position or role through the formal approval or decision of a senate.
- 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_69f76eb1bc508190924e9fa5d8acdeb3 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a037cad051c8190b28b354b89208574 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a11efc08190bb7cacc1325b4dc6 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c842b2c819082f1d2db995ac2eb |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:16 p.m.