Triple
T14238343
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Karamojong |
E352944
|
entity |
| Predicate | relatedGroup |
P37
|
FINISHED |
| Object | Toposa |
E872575
|
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: Toposa | Statement: [Karamojong, relatedGroup, Toposa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Toposa Context triple: [Karamojong, relatedGroup, Toposa]
-
A.
Toposa
chosen
Toposa is a Nilotic ethnic group primarily inhabiting southeastern South Sudan, known for their pastoralist lifestyle and rich oral traditions.
-
B.
Tuspa
Tuspa is an alternative name for Tushpa, the ancient capital city of the Urartian kingdom located near modern-day Lake Van in eastern Turkey.
-
C.
Tafoya
Tafoya is the surname of Michele Tafoya, a prominent American sportscaster best known for her work as an NFL sideline reporter.
-
D.
Orohena
Orohena is the highest peak on the island of Tahiti in French Polynesia, known for its rugged volcanic terrain and prominence in the Society Islands.
-
E.
Tezonco
Tezonco is a metro station in Mexico City that serves the southeastern area of the city on the capital’s rapid transit network.
- 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_69d8278adc7c8190a9218d69bce3c4e6 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de62422e28819089e7115052a28c96 |
completed | April 14, 2026, 3:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd281f80548190ad489c418f27e82c |
completed | May 8, 2026, 12:02 a.m. |
Created at: April 10, 2026, 1:08 a.m.