Triple
T13574467
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tsavo River |
E324245
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object |
Tsavo town
Tsavo town is a small settlement in southeastern Kenya that serves as a gateway to the nearby Tsavo East and Tsavo West National Parks.
|
E1049374
|
NE FINISHED |
How this triple was built (4 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: Tsavo town | Statement: [Tsavo River, near, Tsavo town]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tsavo town Context triple: [Tsavo River, near, Tsavo town]
-
A.
Malindi
Malindi is a historic coastal town in southeastern Kenya known for its beaches, Swahili culture, and role as a former trading port on the Indian Ocean.
-
B.
Omuta
Omuta is an industrial city in southern Fukuoka Prefecture, Japan, historically known for its coal mining and chemical industries.
-
C.
Mbewuleni
Mbewuleni is a rural village in South Africa’s Eastern Cape province, best known as the birthplace of former South African president Thabo Mbeki.
-
D.
Maswa
Maswa is a town and administrative district in northern Tanzania, known for its agricultural activities within the Simiyu Region.
-
E.
Kadoma
Kadoma is a city in central Zimbabwe known for its gold mining and agricultural activities.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Tsavo town Triple: [Tsavo River, near, Tsavo town]
Generated description
Tsavo town is a small settlement in southeastern Kenya that serves as a gateway to the nearby Tsavo East and Tsavo West National Parks.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tsavo town Target entity description: Tsavo town is a small settlement in southeastern Kenya that serves as a gateway to the nearby Tsavo East and Tsavo West National Parks.
-
A.
Malindi
Malindi is a historic coastal town in southeastern Kenya known for its beaches, Swahili culture, and role as a former trading port on the Indian Ocean.
-
B.
Omuta
Omuta is an industrial city in southern Fukuoka Prefecture, Japan, historically known for its coal mining and chemical industries.
-
C.
Mbewuleni
Mbewuleni is a rural village in South Africa’s Eastern Cape province, best known as the birthplace of former South African president Thabo Mbeki.
-
D.
Maswa
Maswa is a town and administrative district in northern Tanzania, known for its agricultural activities within the Simiyu Region.
-
E.
Kadoma
Kadoma is a city in central Zimbabwe known for its gold mining and agricultural activities.
- F. None of above. chosen
Provenance (5 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_69d80769100c819099111274614f5ed2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb02b1f108190a12af382d1de70bb |
completed | April 12, 2026, 2:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f76bba21f88190b8952fb0879e623d |
completed | May 3, 2026, 3:37 p.m. |
| NEDg | Description generation | batch_69f77641e5308190a75bcffeb9bfd7b4 |
completed | May 3, 2026, 4:22 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f77923fd1481908af251a1dcbcf441 |
completed | May 3, 2026, 4:34 p.m. |
Created at: April 9, 2026, 9:48 p.m.