Triple
T35161282
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Awka South Local Government Area |
E1015272
|
entity |
| Predicate | hasElectoralWardCount |
P63824
|
FINISHED |
| Object | multiple electoral wards |
—
|
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 electoral wards | Statement: [Awka South Local Government Area, hasElectoralWardCount, multiple electoral wards]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasElectoralWardCount Context triple: [Awka South Local Government Area, hasElectoralWardCount, multiple electoral wards]
-
A.
hasMunicipalWards
Indicates that a municipality is divided into and associated with one or more municipal wards as its internal administrative units.
-
B.
hasElectoralWardStatus
Indicates that an entity holds the official status or designation of an electoral ward within an administrative or political system.
-
C.
hasNumberOfCouncillors
Indicates the relationship that specifies how many councillors are associated with a given entity.
-
D.
numberOfWards
chosen
Indicates the quantitative relationship specifying how many wards are associated with a given entity.
-
E.
hasNumberOfConstituencies
Indicates the specific count of constituencies associated with an entity.
- 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_69f76ddb3a708190b521ba2970b17178 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69fba78aca4c8190b8f1831e8cc04e06 |
completed | May 6, 2026, 8:41 p.m. |
| PD | Predicate disambiguation | batch_69fba34a65a4819088bac6c17542d71c |
completed | May 6, 2026, 8:23 p.m. |
Created at: May 3, 2026, 4:02 p.m.