Triple

T7016374
Position Surface form Disambiguated ID Type / Status
Subject University of Chittagong E162708 entity
Predicate locatedNear P294 FINISHED
Object Chattogram city E31967 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: Chattogram city | Statement: [University of Chittagong, locatedNear, Chattogram city]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Chattogram city
Context triple: [University of Chittagong, locatedNear, Chattogram city]
  • A. Chittagong chosen
    Chittagong is a major coastal city and Bangladesh’s principal seaport, known for its bustling maritime trade and industrial significance.
  • B. Dhaka
    Dhaka is the capital and largest city of Bangladesh, serving as the country’s political, economic, and cultural center.
  • C. Rangpur
    Rangpur is a city in northern Bangladesh known as a regional administrative, cultural, and commercial center.
  • D. Narayanganj City
    Narayanganj City is a major industrial and river port city in central Bangladesh, known for its textile and jute industries and its proximity to the capital, Dhaka.
  • E. Rajshahi
    Rajshahi is a prominent city in western Bangladesh, known as an important cultural, educational, and commercial center of the Bengal region.
  • 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_69c6885a127c8190867b059bdccf13ff completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6e1d43e948190843f1cef3ce2004e completed March 27, 2026, 8 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7884ac340819093e1812738d5e30f completed March 28, 2026, 7:50 a.m.
Created at: March 27, 2026, 2:34 p.m.