Triple
T3696419
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shira |
E78469
|
entity |
| Predicate | olderThan |
P5873
|
FINISHED |
| Object | Mawenzi |
E78468
|
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: Mawenzi | Statement: [Shira, olderThan, Mawenzi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mawenzi Context triple: [Shira, olderThan, Mawenzi]
-
A.
Mawenzi
chosen
Mawenzi is the jagged, eroded eastern peak of Mount Kilimanjaro and one of its three main volcanic cones.
-
B.
Macheke
Macheke is a small town in eastern Zimbabwe situated along a major route between Harare and Mutare, known for its surrounding agricultural activities.
-
C.
Kibuli Hill
Kibuli Hill is one of the prominent hills in Kampala, Uganda, known for its historic mosque and significant Muslim community institutions.
-
D.
Nyazura
Nyazura is a small town in eastern Zimbabwe situated along the main road and railway linking Harare and Mutare.
-
E.
Nyanga Highlands
Nyanga Highlands is a mountainous region in eastern Zimbabwe known for its scenic landscapes, cool climate, and popular hiking and holiday resorts.
- 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_69ad85e3b1888190abc983e06968696d |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc50f9ad88190a926042fa73d65dc |
completed | March 8, 2026, 6:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b51c688e0c8190a7e9ea3fe010c361 |
completed | March 14, 2026, 8:29 a.m. |
Created at: March 8, 2026, 3:26 p.m.