Triple
T8392193
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Meru |
E197967
|
entity |
| Predicate | isVisibleFrom |
P854
|
FINISHED |
| Object | Arusha city |
E45051
|
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: Arusha city | Statement: [Mount Meru, isVisibleFrom, Arusha city]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Arusha city Context triple: [Mount Meru, isVisibleFrom, Arusha city]
-
A.
Arusha, Tanzania
chosen
Arusha, Tanzania is a major city in northern Tanzania known as a diplomatic hub and gateway to popular safari destinations and Mount Kilimanjaro.
-
B.
Dodoma
Dodoma is the political and administrative capital city of Tanzania, located in the country’s central region.
-
C.
Arusha Region
Arusha Region is an administrative region in northern Tanzania known for its tourism hub city of Arusha and proximity to major national parks and Mount Kilimanjaro.
-
D.
Oshakati
Oshakati is a major northern Namibian town that serves as an important commercial and administrative hub.
-
E.
Moshi
Moshi is a Tanzanian town in the Kilimanjaro Region that serves as a major gateway and base for climbers ascending Mount Kilimanjaro.
- 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_69ca82f749388190bffbea6dfb509016 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb810e16b081908e2c25bfb9d590ed |
completed | March 31, 2026, 8:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cea813e384819090a8a7dc05e41ab2 |
completed | April 2, 2026, 5:32 p.m. |
Created at: March 30, 2026, 6:03 p.m.