Triple
T13705779
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kogi State |
E328635
|
entity |
| Predicate | federalConstituencyCount |
P13200
|
FINISHED |
| Object | multiple federal constituencies |
—
|
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: multiple federal constituencies | Statement: [Kogi State, federalConstituencyCount, multiple federal constituencies]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: federalConstituencyCount Context triple: [Kogi State, federalConstituencyCount, multiple federal constituencies]
-
A.
hasNumberOfConstituencies
chosen
Indicates the specific count of constituencies associated with an entity.
-
B.
numberOfElectorates
Indicates the total count of electoral districts or constituencies associated with a given entity.
-
C.
hasNumberOfSenatorialDivisions
Indicates the relationship that specifies how many senatorial divisions are associated with a given entity.
-
D.
electoralRegionSeatCount
Indicates the number of seats allocated to a given electoral region within a representative body or legislature.
-
E.
legislativeCouncilSeats
Indicates the number of seats held or allocated in a legislative council within a given political or administrative context.
- 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_69d8076ff62081908a7bd79889edd7a0 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dcad17732c8190bbd0d73107711c99 |
completed | April 13, 2026, 8:45 a.m. |
| PD | Predicate disambiguation | batch_69dbbe92d77c81908e0244cffb7f78c5 |
completed | April 12, 2026, 3:47 p.m. |
Created at: April 9, 2026, 9:54 p.m.