Triple

T8585809
Position Surface form Disambiguated ID Type / Status
Subject Mandela National Stadium E203302 entity
Predicate locatedNear P294 FINISHED
Object Kampala E40695 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: Kampala | Statement: [Mandela National Stadium, locatedNear, Kampala]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kampala
Context triple: [Mandela National Stadium, locatedNear, Kampala]
  • A. Kampala chosen
    Kampala is the capital and largest city of Uganda, serving as the country’s political, economic, and cultural center.
  • B. Entebbe
    Entebbe is a town in central Uganda on a peninsula into Lake Victoria, known for its international airport and the site of the 1976 hostage-rescue operation.
  • C. Dodoma
    Dodoma is the political and administrative capital city of Tanzania, located in the country’s central region.
  • 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. 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.
  • 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_69ca8329bb7c8190a63c643730839103 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cc457be9b88190bd9a2fc32350c31e completed March 31, 2026, 10:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf889f91288190b052c4a41359d743 completed April 3, 2026, 9:30 a.m.
Created at: March 30, 2026, 6:22 p.m.