Triple

T294123
Position Surface form Disambiguated ID Type / Status
Subject South Asia E6055 entity
Predicate hasMajorCity P316 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: [South Asia, hasMajorCity, Dhaka]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dhaka
Context triple: [South Asia, hasMajorCity, 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. Calcutta
    Calcutta, now known as Kolkata, is a major cultural and commercial metropolis in eastern India that served as the capital of British India until the early 20th century.
  • 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_69a2e79114b081909490b3bf5a5dbb51 completed Feb. 28, 2026, 1:03 p.m.
NER Named-entity recognition batch_69a2e978420881908488df342a7d5e90 completed Feb. 28, 2026, 1:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3a5d514388190ac0f748a406ae43e completed March 1, 2026, 2:35 a.m.
Created at: Feb. 28, 2026, 1:06 p.m.