Triple
T28258389
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Government of the Province of Buenos Aires |
E712512
|
entity |
| Predicate | hasBicameralLegislature |
P1312
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Government of the Province of Buenos Aires, hasBicameralLegislature, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBicameralLegislature Context triple: [Government of the Province of Buenos Aires, hasBicameralLegislature, true]
-
A.
hasBicameralStructure
chosen
Indicates that an entity possesses a two-chamber (two-house) internal organizational or legislative structure.
-
B.
hasUnicameralOrBicameralContext
Indicates that an entity is associated with a legislative context specifying whether it is unicameral or bicameral.
-
C.
isBicameralPartnerOf
Indicates that one legislative body or chamber forms one of the two cooperating parts of a bicameral partnership with another.
-
D.
isBicameralJointCommittee
Indicates that a committee is a joint body composed of members from both chambers of a bicameral legislature.
-
E.
isUnicameral
Indicates that a legislative body consists of a single chamber or house, rather than multiple separate chambers.
- 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_69efb5207eb08190827e4c34048030b1 |
completed | April 27, 2026, 7:12 p.m. |
| NER | Named-entity recognition | batch_69f6978fe97081908fe568091ad9b159 |
completed | May 3, 2026, 12:32 a.m. |
| PD | Predicate disambiguation | batch_69f69661e6ec8190948251c7516a32ad |
completed | May 3, 2026, 12:27 a.m. |
Created at: April 27, 2026, 11:09 p.m.