Triple
T5884145
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Federal Senate of Brazil |
E130819
|
entity |
| Predicate | seatsForFederalDistrict |
P59176
|
FINISHED |
| Object | 3 |
—
|
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: 3 | Statement: [Federal Senate of Brazil, seatsForFederalDistrict, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: seatsForFederalDistrict Context triple: [Federal Senate of Brazil, seatsForFederalDistrict, 3]
-
A.
electoralRegionSeatCount
chosen
Indicates the number of seats allocated to a given electoral region within a representative body or legislature.
-
B.
eachDistrictElects
Indicates that every electoral district selects or chooses its own representative or set of representatives.
-
C.
includesFederalDistrict
Indicates that one administrative or geographic entity contains or encompasses a federal district within its boundaries.
-
D.
numberOfDistricts
Indicates the total count of districts associated with a given entity or area.
-
E.
maximumSeatsPerState
Indicates the upper limit on the number of seats that any single state is allowed to have.
- 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_69c0085628dc8190b334c1b44c067efc |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03fe07b7081909f8577ec3a9a1a8d |
completed | March 22, 2026, 7:15 p.m. |
| PD | Predicate disambiguation | batch_69c0334bdc308190ad0d7199ab975588 |
completed | March 22, 2026, 6:22 p.m. |
Created at: March 22, 2026, 3:57 p.m.