Triple

T5547422
Position Surface form Disambiguated ID Type / Status
Subject Mary Ellen Trainor E145441 entity
Predicate notableWork P4 FINISHED
Object Congo E45036 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: Congo | Statement: [Mary Ellen Trainor, notableWork, Congo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Congo
Context triple: [Mary Ellen Trainor, notableWork, Congo]
  • A. Congo chosen
    Congo is a Central African country whose economy is heavily reliant on oil production and exports.
  • B. Kongō
    Kongō was a Japanese Kongō-class fast battleship that served prominently in the Imperial Japanese Navy during World War II.
  • C. Kongo
    Kongo refers to the Central African ethnic and cultural group and historical kingdom whose traditions and beliefs have significantly influenced Afro-diasporic religions in the Americas.
  • D. Congo River
    The Congo River is Africa’s second-longest river and the world’s deepest, flowing through central Africa to the Atlantic Ocean and serving as a major waterway for transport, ecology, and regional economies.
  • E. Lualaba River
    The Lualaba River is the upper course of the Congo River in the Democratic Republic of the Congo, flowing through the southeast of the country and serving as a key waterway in Central Africa.
  • 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_69c008fb879c81909f5bfa56fadc1d46 completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c01fe0244c8190aeb995f79f22a039 completed March 22, 2026, 4:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69c04cf1c66c819099e2cde5e1c7bec0 completed March 22, 2026, 8:11 p.m.
Created at: March 22, 2026, 3:35 p.m.