Triple
T3483609
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Niger State |
E73553
|
entity |
| Predicate | hasLocalGovernmentAreasCount |
P41115
|
FINISHED |
| Object | 25 |
—
|
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: 25 | Statement: [Niger State, hasLocalGovernmentAreasCount, 25]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLocalGovernmentAreasCount Context triple: [Niger State, hasLocalGovernmentAreasCount, 25]
-
A.
hasLocalGovernmentAreas
Indicates that an entity is administratively divided into, or associated with, one or more local government areas.
-
B.
numberOfLocalGovernmentAreas
chosen
Indicates the count of local government areas associated with a given entity or region.
-
C.
hasLocalGovernmentAreaCode
Indicates that an entity is associated with a specific local government area identified by a particular code.
-
D.
hasLocalGovernmentAreaStatus
Indicates that an entity holds the official designation or recognition as a local government area within a defined administrative system.
-
E.
hasNumberOfMunicipalities
Indicates the relationship that specifies how many municipalities are associated with or contained within a given administrative or geographic 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_69ad85b3c9b08190857cae74c7f36da9 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adbb781e9c8190810fdd814f506127 |
completed | March 8, 2026, 6:10 p.m. |
| PD | Predicate disambiguation | batch_69adae0935ac8190bfa8a8bd3dcd3301 |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:17 p.m.