Triple
T16206703
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | R61 route |
E393345
|
entity |
| Predicate | passesThrough |
P225
|
FINISHED |
| Object | Lusikisiki |
E879465
|
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: Lusikisiki | Statement: [R61 route, passesThrough, Lusikisiki]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lusikisiki Context triple: [R61 route, passesThrough, Lusikisiki]
-
A.
Lusikisiki
chosen
Lusikisiki is a small rural town in South Africa’s Eastern Cape, known for its scenic coastal surroundings and role as a local service and administrative center.
-
B.
Kilembe
Kilembe is a town in western Uganda that serves as a common starting point for treks to the Rwenzori Mountains, including ascents of Margherita Peak.
-
C.
Nabulungi
Nabulungi is a central character in the musical "The Book of Mormon," a hopeful and idealistic young Ugandan woman who becomes a key follower of the missionaries’ teachings.
-
D.
Luyengo
Luyengo is a locality in Eswatini known for hosting the Luyengo Campus of the University of Eswatini and its agricultural education facilities.
-
E.
Kibondo
Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
- 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_69d87f1f5bd08190bd01cac0d5b9d2ef |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e227101a3c819095ef40e50bf66433 |
completed | April 17, 2026, 12:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00078fa2ac8190a0a2cf38bc41498d |
completed | May 10, 2026, 4:20 a.m. |
Created at: April 10, 2026, 5:03 a.m.