Triple
T15745840
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rift Valley Province |
E381719
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object |
Kabarnet
Kabarnet is a town in Kenya that serves as an administrative and commercial center in the Rift Valley region.
|
E1174605
|
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: Kabarnet | Statement: [Rift Valley Province, containsCity, Kabarnet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kabarnet Context triple: [Rift Valley Province, containsCity, Kabarnet]
-
A.
Kablar
Kablar is a prominent mountain in central Serbia, known for its scenic views over the West Morava River and proximity to the city of Čačak.
-
B.
Kabelsketal
Kabelsketal is a municipality in the Saalekreis district of Saxony-Anhalt in central Germany, situated near the city of Halle (Saale).
-
C.
Inetkaes
Inetkaes was an ancient Egyptian royal woman, likely a princess of the early Old Kingdom period.
-
D.
Syntu Ksiar
Syntu Ksiar is a scenic riverside spot in Meghalaya’s Jaintia Hills, known for its natural beauty and historical significance as a center of the region’s freedom struggle.
-
E.
Cencibel
Cencibel is a Spanish red wine grape variety, better known internationally as Tempranillo, used to produce full-bodied, age-worthy wines.
- 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: Kabarnet Triple: [Rift Valley Province, containsCity, Kabarnet]
Generated description
Kabarnet is a town in Kenya that serves as an administrative and commercial center in the Rift Valley region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kabarnet Target entity description: Kabarnet is a town in Kenya that serves as an administrative and commercial center in the Rift Valley region.
-
A.
Kablar
Kablar is a prominent mountain in central Serbia, known for its scenic views over the West Morava River and proximity to the city of Čačak.
-
B.
Kabelsketal
Kabelsketal is a municipality in the Saalekreis district of Saxony-Anhalt in central Germany, situated near the city of Halle (Saale).
-
C.
Inetkaes
Inetkaes was an ancient Egyptian royal woman, likely a princess of the early Old Kingdom period.
-
D.
Syntu Ksiar
Syntu Ksiar is a scenic riverside spot in Meghalaya’s Jaintia Hills, known for its natural beauty and historical significance as a center of the region’s freedom struggle.
-
E.
Cencibel
Cencibel is a Spanish red wine grape variety, better known internationally as Tempranillo, used to produce full-bodied, age-worthy wines.
- 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_69d86d9e6b44819085d1f6a969ecb74c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0502c0c3c8190b8e512df307039c1 |
completed | April 16, 2026, 2:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff8307824881909ba85e4c3da65d28 |
completed | May 9, 2026, 6:55 p.m. |
| NEDg | Description generation | batch_69ff83cb02f48190af506724f3ffb73e |
completed | May 9, 2026, 6:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff844fa00c8190a47eb46394db097b |
completed | May 9, 2026, 7 p.m. |
Created at: April 10, 2026, 4:46 a.m.