Triple

T10730804
Position Surface form Disambiguated ID Type / Status
Subject Muhammad Yunus E253065 entity
Predicate residence P75 FINISHED
Object Dhaka E26021 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: Dhaka | Statement: [Muhammad Yunus, residence, Dhaka]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dhaka
Context triple: [Muhammad Yunus, residence, Dhaka]
  • A. Dhaka chosen
    Dhaka is the capital and largest city of Bangladesh, serving as the country’s political, economic, and cultural center.
  • B. Chittagong
    Chittagong is a major coastal city and Bangladesh’s principal seaport, known for its bustling maritime trade and industrial significance.
  • C. Rangpur
    Rangpur is a city in northern Bangladesh known as a regional administrative, cultural, and commercial center.
  • D. Greater Dhaka
    Greater Dhaka is the densely populated metropolitan region centered on Bangladesh’s capital city, Dhaka, encompassing its urban core and surrounding suburban and peri-urban areas.
  • E. Dhaka District
    Dhaka District is an administrative region in central Bangladesh that encompasses the nation’s capital city and serves as a major political, economic, and cultural hub.
  • 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_69d6aa5d8be481909a43218b2bfdbe95 completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d70fcb1cd881909635def59ad5d19c completed April 9, 2026, 2:32 a.m.
NED1 Entity disambiguation (via context triple) batch_69de22a97fec8190bb97f68353c2144e completed April 14, 2026, 11:19 a.m.
Created at: April 8, 2026, 9:14 p.m.