Triple

T15599827
Position Surface form Disambiguated ID Type / Status
Subject Machame Gate E375001 entity
Predicate hasAccessRoadFrom P22549 FINISHED
Object Moshi town E76525 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: Moshi town | Statement: [Machame Gate, hasAccessRoadFrom, Moshi town]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Moshi town
Context triple: [Machame Gate, hasAccessRoadFrom, Moshi town]
  • A. Moshi chosen
    Moshi is a Tanzanian town in the Kilimanjaro Region that serves as a major gateway and base for climbers ascending Mount Kilimanjaro.
  • B. Juja
    Juja is a rapidly growing urban town in Kenya known for its proximity to Nairobi and its major universities and industries.
  • C. 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.
  • D. Limuru
    Limuru is a highland town in central Kenya known for its cool climate, tea plantations, and proximity to Nairobi.
  • E. Zanzibar City
    Zanzibar City is the historic and administrative capital of Zanzibar, Tanzania, renowned for its UNESCO-listed Stone Town and rich Swahili, Arab, and colonial heritage.
  • 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_69d85cce25008190b13b52745fbd719b completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04e621fc4819097e8e85e7ddfdc6c completed April 16, 2026, 2:50 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff5f355ff48190a2c2c262c09e6de0 completed May 9, 2026, 4:22 p.m.
Created at: April 10, 2026, 4:12 a.m.