Triple

T632011
Position Surface form Disambiguated ID Type / Status
Subject Islamic world E15944 entity
Predicate hasCulturalCenter P2412 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: [Islamic world, hasCulturalCenter, Dhaka]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dhaka
Context triple: [Islamic world, hasCulturalCenter, 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. Rajshahi
    Rajshahi is a prominent city in western Bangladesh, known as an important cultural, educational, and commercial center of the Bengal region.
  • D. Khulna
    Khulna is a major industrial and port city in southwestern Bangladesh, situated on the Rupsha and Bhairab rivers and serving as a key gateway to the Sundarbans mangrove forest.
  • E. Murshidabad
    Murshidabad is a historic city in West Bengal, India, that served as the capital of the Nawabs of Bengal and a major political and commercial center during the Mughal and early British periods.
  • 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_69a4935c131c8190a5378c6bf101e8cc completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a49ec2a4c08190bc5c6ce8a10b0967 completed March 1, 2026, 8:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69a56c4d84d8819095afbf0ee9c7bd82 completed March 2, 2026, 10:54 a.m.
Created at: March 1, 2026, 7:35 p.m.