Triple
T9697698
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luo people of Tanzania |
E234694
|
entity |
| Predicate | selfIdentification |
P4296
|
FINISHED |
| Object | Luo |
E45322
|
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: Luo | Statement: [Luo people of Tanzania, selfIdentification, Luo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Luo Context triple: [Luo people of Tanzania, selfIdentification, Luo]
-
A.
Luo
chosen
Luo is a Nilotic language spoken primarily by the Luo people of East Africa, especially in Kenya, Uganda, and Tanzania.
-
B.
Luoyi
Luoyi was an ancient Chinese city that served as a major political and cultural center of the Zhou dynasty.
-
C.
Yangluo
Yangluo is a town in Wuhan, Hubei Province, China, known as an industrial and port area along the Yangtze River.
-
D.
Lingbo
Lingbo is a small village in central Sweden located within Ockelbo Municipality in Gävleborg County.
-
E.
Liang
Liang is a common Chinese surname borne by numerous historical figures, scholars, and public personalities across the Chinese-speaking world.
- 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_69ca84cb580c8190a7e5f4b3bcdaf2a4 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9d3c02e0819098d05c68805689f1 |
completed | April 1, 2026, 10:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1af8990608190a841c1fbdb22eb71 |
completed | April 5, 2026, 12:40 a.m. |
Created at: March 30, 2026, 8:18 p.m.