Triple

T20904604
Position Surface form Disambiguated ID Type / Status
Subject Manikganj District E514760 entity
Predicate hasCapital P204 FINISHED
Object Manikganj
Manikganj is a town in central Bangladesh that serves as the administrative and commercial hub of Manikganj District.
 E1456877 NE FINISHED

How this triple was built (4 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: Manikganj | Statement: [Manikganj District, hasCapital, Manikganj]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Manikganj
Context triple: [Manikganj District, hasCapital, Manikganj]
  • A. Keraniganj
    Keraniganj is a suburban upazila of Dhaka, Bangladesh, known for its dense population, river-based commerce, and numerous garment and brick industries.
  • B. Khandhaka
    Khandhaka is a major section of the Buddhist Vinaya literature that details monastic rules, procedures, and communal regulations for the Sangha.
  • C. Sunamganj
    Sunamganj is a town and district headquarters in northeastern Bangladesh, known for its wetlands, haor landscapes, and cultural ties to the Greater Sylhet region.
  • D. Begumganj
    Begumganj is a town and administrative subdivision in the Raisen district of Madhya Pradesh, India.
  • E. Khanakul
    Khanakul is a town in the Hooghly district of West Bengal, India, known for its rural setting and local agricultural economy.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Manikganj
Triple: [Manikganj District, hasCapital, Manikganj]
Generated description
Manikganj is a town in central Bangladesh that serves as the administrative and commercial hub of Manikganj District.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Manikganj
Target entity description: Manikganj is a town in central Bangladesh that serves as the administrative and commercial hub of Manikganj District.
  • A. Keraniganj
    Keraniganj is a suburban upazila of Dhaka, Bangladesh, known for its dense population, river-based commerce, and numerous garment and brick industries.
  • B. Khandhaka
    Khandhaka is a major section of the Buddhist Vinaya literature that details monastic rules, procedures, and communal regulations for the Sangha.
  • C. Sunamganj
    Sunamganj is a town and district headquarters in northeastern Bangladesh, known for its wetlands, haor landscapes, and cultural ties to the Greater Sylhet region.
  • D. Begumganj
    Begumganj is a town and administrative subdivision in the Raisen district of Madhya Pradesh, India.
  • E. Khanakul
    Khanakul is a town in the Hooghly district of West Bengal, India, known for its rural setting and local agricultural economy.
  • F. None of above. chosen

Provenance (5 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_69e0b4f8a1108190bce3d31331290ced completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6e8ff36488190987ecdfcbed4220c completed April 21, 2026, 3:03 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0918d86e0c8190b72886c8eefeee7f completed May 17, 2026, 1:24 a.m.
NEDg Description generation batch_6a091aa1c728819086154d9975acb981 completed May 17, 2026, 1:32 a.m.
NED2 Entity disambiguation (via description) batch_6a091b17eec481908b0da1432856efbf completed May 17, 2026, 1:34 a.m.
Created at: April 16, 2026, 12:47 p.m.