Triple
T4813188
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chamber of Deputies (Brazil) |
E107119
|
entity |
| Predicate | maximumSeatsPerState |
P59785
|
FINISHED |
| Object | 70 |
—
|
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: 70 | Statement: [Chamber of Deputies (Brazil), maximumSeatsPerState, 70]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maximumSeatsPerState Context triple: [Chamber of Deputies (Brazil), maximumSeatsPerState, 70]
-
A.
minimumSeatsPerCanton
Indicates the minimum number of seats that must be allocated to each canton in a representative body or distribution scheme.
-
B.
numberOfSeatsInSenate
Indicates the total count of seats allocated in a given senate.
-
C.
numberOfRepresentatives
Indicates the quantity of representatives associated with a given entity or unit.
-
D.
numberOfStatesRepresented
Indicates how many distinct states are represented or covered in a given context or entity.
-
E.
electoralRegionSeatCount
Indicates the number of seats allocated to a given electoral region within a representative body or legislature.
- 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_69bd43f779448190b92885cb70abb6c2 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6ddd17d881909f7731ff2b460e83 |
completed | March 20, 2026, 3:55 p.m. |
| PD | Predicate disambiguation | batch_69bd6c1dfa3481909d240d50ed0ee38c |
completed | March 20, 2026, 3:47 p.m. |
| PDg | Predicate description generation | batch_69bd6dda5e808190a26ec85e4499d8e4 |
completed | March 20, 2026, 3:55 p.m. |
Created at: March 20, 2026, 1:23 p.m.