Triple
T8232266
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Constitutional Court of Spain |
E192320
|
entity |
| Predicate | numberAppointedByCongress |
P80986
|
FINISHED |
| Object | 4 |
—
|
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 | Statement: [Constitutional Court of Spain, numberAppointedByCongress, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberAppointedByCongress Context triple: [Constitutional Court of Spain, numberAppointedByCongress, 4]
-
A.
congressionalNumber
Indicates the specific numbered session or term of a legislative congress to which an entity is associated.
-
B.
appointedBy
Indicates that one entity has been formally selected or assigned to a position, role, or office by another entity.
-
C.
congressionalNumberingState
Indicates the state associated with a particular numbering or designation of a congressional body or district.
-
D.
apportionedBy
Indicates that something is divided or allocated among parts or recipients according to a specified agent, rule, or method.
-
E.
numberOfRepresentatives
Indicates the quantity of representatives associated with a given entity or unit.
- 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_69ca82db5b90819085d1ad7c2e27bfcc |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7824a6c48190a3abb76c4c3dcc71 |
completed | March 31, 2026, 7:30 a.m. |
| PD | Predicate disambiguation | batch_69cb36b1dea0819091418072501e79c1 |
completed | March 31, 2026, 2:51 a.m. |
| PDg | Predicate description generation | batch_69cb3d6c34708190a987d68529cbb0b3 |
completed | March 31, 2026, 3:20 a.m. |
Created at: March 30, 2026, 5:46 p.m.