Triple
T2887221
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cross River State |
E59533
|
entity |
| Predicate | hasLocalGovernmentAreaCount |
P41115
|
FINISHED |
| Object | 18 |
—
|
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: 18 | Statement: [Cross River State, hasLocalGovernmentAreaCount, 18]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLocalGovernmentAreaCount Context triple: [Cross River State, hasLocalGovernmentAreaCount, 18]
-
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.
hasLocalGovernmentBody
Indicates that an entity is administered or overseen by a specific local government authority or governing body.
-
D.
hasNumberOfMunicipalities
Indicates the relationship that specifies how many municipalities are associated with or contained within a given administrative or geographic entity.
-
E.
numberOfAreaCouncils
Indicates the total count of area councils associated with a given 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_69ab4ac739188190a112f42a5a69c951 |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abe047aa7c8190a0ed570c13f3a1a2 |
completed | March 7, 2026, 8:22 a.m. |
| PD | Predicate disambiguation | batch_69abdd15cbf08190bf7fea5ea516848a |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 10:03 p.m.