Triple

T22282097
Position Surface form Disambiguated ID Type / Status
Subject Magura District E550758 entity
Predicate borderedBy P224 FINISHED
Object Faridpur District NE NERFINISHED

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: Faridpur District | Statement: [Magura District, borderedBy, Faridpur District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Faridpur District
Context triple: [Magura District, borderedBy, Faridpur District]
  • A. Kishoreganj District
    Kishoreganj District is an administrative district in central Bangladesh known for its riverine landscape, cultural heritage, and agricultural economy.
  • B. Munshiganj District
    Munshiganj District is an administrative district in central Bangladesh known for its historical significance and proximity to the capital, Dhaka.
  • C. Faridpur chosen
    Faridpur is a historic town in central Bangladesh known for its cultural heritage and role in the Bengal Renaissance.
  • D. Faridpur
    Faridpur is a town in the Bareilly district of Uttar Pradesh, India, known as a local commercial and administrative center for the surrounding rural region.
  • E. Sirajganj District
    Sirajganj District is an administrative district in north-central Bangladesh, located along the Jamuna River and known for its agriculture and handloom weaving.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e11e44d538819097c6b8f333af3352 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f14eac0994819088e39a1b5d39cf18 completed April 29, 2026, 12:19 a.m.
Created at: April 16, 2026, 8:40 p.m.