Triple

T3474366
Position Surface form Disambiguated ID Type / Status
Subject The African Queen E73334 entity
Predicate filmingLocation P40 FINISHED
Object Uganda E10768 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: Uganda | Statement: [The African Queen, filmingLocation, Uganda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Uganda
Context triple: [The African Queen, filmingLocation, Uganda]
  • A. Uganda chosen
    Uganda is a landlocked country in East Africa known for its diverse landscapes, abundant wildlife, and location along the equator.
  • B. Uganda and Democratic Republic of the Congo
    Uganda and the Democratic Republic of the Congo are neighboring Central-East African countries that share extensive natural frontiers, rich biodiversity, and significant cross-border cultural and economic ties.
  • C. Oluganda
    Oluganda is the endonym for Luganda, a major Bantu language spoken primarily by the Baganda people in central Uganda.
  • D. Nzera
    Nzera is a settlement located within Tanzania’s Geita Region in East Africa.
  • E. Kenya
    Kenya is an East African country known for its diverse wildlife, scenic landscapes from savannas to highlands, and a coastline along the Indian Ocean.
  • 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_69ad85b2fed48190948c8765e453d270 completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adbb580d4c819080bcc0bccd1e18e2 completed March 8, 2026, 6:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69b3680763608190acdd146dc7c0b239 completed March 13, 2026, 1:27 a.m.
Created at: March 8, 2026, 3:17 p.m.