Triple

T23500816
Position Surface form Disambiguated ID Type / Status
Subject A104 highway E571836 entity
Predicate connectsCity P4245 FINISHED
Object Nairobi E6371 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: Nairobi | Statement: [A104 highway, connectsCity, Nairobi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nairobi
Context triple: [A104 highway, connectsCity, Nairobi]
  • A. Nairobi chosen
    Nairobi is the capital and largest city of Kenya, serving as a major political, economic, and cultural hub in East Africa.
  • B. Nairobi
    Nairobi is a fan-favorite character from the Spanish series "Money Heist," known for her sharp leadership, optimism, and expertise in overseeing the gang’s money-printing operations.
  • C. Yaunde
    Yaunde is an alternative name for Kolo, likely referring to the same place or entity under a different local or historical designation.
  • D. Lipa City
    Lipa City is a highly urbanized city in Batangas, Philippines, known as a commercial, educational, and religious center in the Calabarzon region.
  • E. Kabete
    Kabete is a prominent town in Kenya’s Central Region, situated within Kiambu County and known for its agricultural activity and proximity to Nairobi.
  • 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_69e245b4829881909b77a70e942bbd54 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f1a8fc37908190af86a01ab85737d6 completed April 29, 2026, 6:45 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0c8275e2bc8190a908d41a9824f2f8 completed May 19, 2026, 3:32 p.m.
Created at: April 17, 2026, 6:06 p.m.