Triple
T13843514
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bushenyi |
E332729
|
entity |
| Predicate | countryCode |
P208
|
FINISHED |
| Object | UG |
E327487
|
NE 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: UG | Statement: [Bushenyi, countryCode, UG]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: UG Context triple: [Bushenyi, countryCode, UG]
-
A.
UG
chosen
UG is the ISO 3166-1 alpha-2 country code for Uganda, used in international standards and country abbreviations.
-
B.
UG
UG is the commonly used abbreviation for the University of Guanajuato, a major public higher education institution in the Mexican state of Guanajuato.
-
C.
UNG
UNG is a public university in Georgia known for its strong focus on leadership development, military programs, and accessible undergraduate education across multiple campuses.
-
D.
Ug
Ug is a shape-shifting intergalactic bounty hunter featured in the sci-fi horror comedy film "Critters 2: The Main Course."
-
E.
GU
GU is an alternative name or abbreviation for the Gated Recurrent Unit, a type of recurrent neural network architecture used in deep learning for sequence modeling tasks.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d81c5ba13c8190839315f54768acfd |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de02afce788190a74dce4e6a3569fa |
completed | April 14, 2026, 9:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7b8f87c188190b90faf7678cb9ad4 |
completed | May 3, 2026, 9:07 p.m. |
Created at: April 9, 2026, 10:13 p.m.