Triple
T13052546
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Parliamentary Building, Kampala |
E327481
|
entity |
| Predicate | legislativeBodyTypeServed |
P75281
|
FINISHED |
| Object | unicameral parliament |
—
|
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: unicameral parliament | Statement: [Parliamentary Building, Kampala, legislativeBodyTypeServed, unicameral parliament]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legislativeBodyTypeServed Context triple: [Parliamentary Building, Kampala, legislativeBodyTypeServed, unicameral parliament]
-
A.
legislatureTypeServed
chosen
Indicates the type of legislative body in which an entity has served (e.g., national, state, or local legislature).
-
B.
stateLegislativeBodyServedIn
Indicates the specific state-level legislative body in which an individual has served as a member.
-
C.
legislatureServed
Indicates that an individual has served as a member of a specific legislative body during some period of time.
-
D.
governmentTypeServed
Indicates the type of government that an entity serves, is affiliated with, or operates under in its role or function.
-
E.
legislativeChamberServed
Indicates that an individual has served as a member of a specified legislative chamber.
- 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_69d8076e64308190904fb5c93517c901 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d98a9829b48190b23624b6b3df4600 |
completed | April 10, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_69d9803aca4c8190b1015cd159cc47a9 |
completed | April 10, 2026, 10:56 p.m. |
Created at: April 9, 2026, 8:58 p.m.