Triple
T2694188
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rivers State |
E58472
|
entity |
| Predicate | numberOfLocalGovernmentAreas |
P41115
|
FINISHED |
| Object | 23 |
—
|
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: 23 | Statement: [Rivers State, numberOfLocalGovernmentAreas, 23]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfLocalGovernmentAreas Context triple: [Rivers State, numberOfLocalGovernmentAreas, 23]
-
A.
numberOfRegionalCouncils
Indicates the total count of regional councils associated with a given entity.
-
B.
governingSubdivision
Indicates that one administrative or political unit serves as the governing authority over a smaller, subordinate territorial subdivision.
-
C.
hasNumberOfBarangays
Indicates the total count of barangays associated with a given administrative unit or locality.
-
D.
hasNumberOfMunicipalities
Indicates the relationship that specifies how many municipalities are associated with or contained within a given administrative or geographic entity.
-
E.
numberOfDistricts
Indicates the total count of districts associated with a given entity or area.
- 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_69ab4ac269e481909cb317d79e68b75b |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abda10a9bc81908473d02ab9116cef |
completed | March 7, 2026, 7:56 a.m. |
| PD | Predicate disambiguation | batch_69abd81ea5d88190ab5c8f8b8064b931 |
completed | March 7, 2026, 7:47 a.m. |
| PDg | Predicate description generation | batch_69abd879bb808190bd2c34de1664c816 |
completed | March 7, 2026, 7:49 a.m. |
Created at: March 6, 2026, 9:55 p.m.