Triple
T12989495
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nigerian Army University Biu |
E321864
|
entity |
| Predicate | nearbyInfluence |
P107920
|
FINISHED |
| Object | Biu local economy |
—
|
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: Biu local economy | Statement: [Nigerian Army University Biu, nearbyInfluence, Biu local economy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nearbyInfluence Context triple: [Nigerian Army University Biu, nearbyInfluence, Biu local economy]
-
A.
nearbyTo
Indicates that one entity is located close in distance or position to another entity.
-
B.
nearbyLocation
Indicates that one location is situated close to another location in physical space.
-
C.
nearbyResourcePotential
Indicates that a resource is likely available or exploitable in the vicinity of the referenced entity or location.
-
D.
nearbyEnvironment
Indicates that an entity is located in or affected by the immediate surrounding conditions or context of another entity.
-
E.
nearbyUrbanCenter
Indicates that one location is geographically close to an urban center, such as a city or large town.
- 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_69d8076479b8819090afce3591939cdf |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97f2a71a0819098bb6cf8a4b2208a |
completed | April 10, 2026, 10:52 p.m. |
| PD | Predicate disambiguation | batch_69d97dbdd94c8190ac4bbecca02dc77b |
completed | April 10, 2026, 10:46 p.m. |
| PDg | Predicate description generation | batch_69d97f1badac8190a59e60751f47b8d6 |
completed | April 10, 2026, 10:52 p.m. |
Created at: April 9, 2026, 8:43 p.m.