Triple

T14927346
Position Surface form Disambiguated ID Type / Status
Subject Portuguese expansion in the Indian Ocean E372165 entity
Predicate hasCenter P35 FINISHED
Object Mombasa E47207 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: Mombasa | Statement: [Portuguese expansion in the Indian Ocean, hasCenter, Mombasa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mombasa
Context triple: [Portuguese expansion in the Indian Ocean, hasCenter, Mombasa]
  • A. Mombasa chosen
    Mombasa is a major coastal city in Kenya known as a key regional port and historic trading hub on the Indian Ocean.
  • B. 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.
  • C. Dar es Salaam
    Dar es Salaam is a major coastal metropolis on the Indian Ocean and the principal economic and commercial hub of Tanzania.
  • D. Port of Mombasa
    The Port of Mombasa is Kenya’s largest and busiest seaport, serving as a key gateway for maritime trade in East and Central Africa.
  • E. Juja
    Juja is a rapidly growing urban town in Kenya known for its proximity to Nairobi and its major universities and industries.
  • 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_69d85cc9da0c81908d583ca3f63a3908 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded633da0c8190b39f606212e48e71 completed April 15, 2026, 12:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe72c4f9c481909642efccb29f71d4 completed May 8, 2026, 11:33 p.m.
Created at: April 10, 2026, 2:35 a.m.