Triple

T4007233
Position Surface form Disambiguated ID Type / Status
Subject Karura Forest E89553 entity
Predicate near P350 FINISHED
Object Runda
Runda is an affluent residential suburb in Nairobi, Kenya, known for its large gated homes, embassies, and proximity to natural areas like Karura Forest.
 E406524 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: Runda | Statement: [Karura Forest, near, Runda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Runda
Context triple: [Karura Forest, near, Runda]
  • A. Rola
    Rola is the wartime nickname of Michał Rola-Żymierski, a Polish military leader and later communist-era Marshal of Poland.
  • B. Łeba
    Łeba is a river in northern Poland that flows through the Pomeranian region to the Baltic Sea.
  • C. Ujazdów
    Ujazdów is a historic neighborhood in central Warsaw, known for its palaces, government buildings, and extensive green areas including parks and gardens.
  • D. Strzelno
    Strzelno is a town in north-central Poland best known as the birthplace of Nobel Prize–winning physicist Albert A. Michelson.
  • E. Lubin
    Lubin is a town in southwestern Poland known for its copper mining industry and location within the Lower Silesian region.
  • 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: Runda
Triple: [Karura Forest, near, Runda]
Generated description
Runda is an affluent residential suburb in Nairobi, Kenya, known for its large gated homes, embassies, and proximity to natural areas like Karura Forest.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Runda
Target entity description: Runda is an affluent residential suburb in Nairobi, Kenya, known for its large gated homes, embassies, and proximity to natural areas like Karura Forest.
  • A. Rola
    Rola is the wartime nickname of Michał Rola-Żymierski, a Polish military leader and later communist-era Marshal of Poland.
  • B. Łeba
    Łeba is a river in northern Poland that flows through the Pomeranian region to the Baltic Sea.
  • C. Ujazdów
    Ujazdów is a historic neighborhood in central Warsaw, known for its palaces, government buildings, and extensive green areas including parks and gardens.
  • D. Strzelno
    Strzelno is a town in north-central Poland best known as the birthplace of Nobel Prize–winning physicist Albert A. Michelson.
  • E. Lubin
    Lubin is a town in southwestern Poland known for its copper mining industry and location within the Lower Silesian region.
  • 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_69aed9585e788190bec2d39deba3750f completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefa62d0e081909aaed2978a840734 completed March 9, 2026, 4:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69b54c67eddc819097eff878c860f3d5 completed March 14, 2026, 11:54 a.m.
NEDg Description generation batch_69b54d1898d48190b717a17ad366918d completed March 14, 2026, 11:57 a.m.
NED2 Entity disambiguation (via description) batch_69b54de8dc708190b83978b15aed2e13 completed March 14, 2026, noon
Created at: March 9, 2026, 3:34 p.m.