Triple
T18074166
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kingdom of Bunyoro |
E432509
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Toro |
—
|
NE NERFINISHED |
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: Toro | Statement: [Kingdom of Bunyoro, borderedBy, Toro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Toro Context triple: [Kingdom of Bunyoro, borderedBy, Toro]
-
A.
Toro
Toro is the official bull-themed mascot of the NFL's Houston Texans, known for entertaining fans at games and team events.
-
B.
Toro
Toro is the bull mascot representing California State University, Dominguez Hills at its athletic events and campus activities.
-
C.
Toro
Toro is the traditional nickname of Torino F.C., a historic Italian football club based in Turin.
-
D.
Toro
chosen
Toro is a traditional kingdom and region in western Uganda, known for its rich cultural heritage and historical monarchy.
-
E.
Toro
Toro is a modernist sculpture by Henri Gaudier-Brzeska that exemplifies his dynamic, abstract approach to representing animal forms.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9070cac81909fa9473fb1c3f1c7 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4ccefcdc4819086d0b224731bfc4d |
completed | April 19, 2026, 12:39 p.m. |
Created at: April 10, 2026, 10:26 a.m.