Triple

T16090590
Position Surface form Disambiguated ID Type / Status
Subject Operation Searchlight E390349 entity
Predicate location P40 FINISHED
Object Rangpur E510413 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: Rangpur | Statement: [Operation Searchlight, location, Rangpur]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rangpur
Context triple: [Operation Searchlight, location, Rangpur]
  • A. Rangpur chosen
    Rangpur is a city in northern Bangladesh known as a regional administrative, cultural, and commercial center.
  • B. Chittagong
    Chittagong is a major coastal city and Bangladesh’s principal seaport, known for its bustling maritime trade and industrial significance.
  • C. Sylhet
    Sylhet is a historically and culturally significant city and region in northeastern Bangladesh, known for its tea gardens, lush landscapes, and role as a major economic and spiritual center.
  • D. Barisal
    Barisal is a major city in southern Bangladesh, historically known as a cultural and riverine hub of the Bengal region.
  • E. Comilla
    Comilla is a major city in eastern Bangladesh known for its historical sites, educational institutions, and role as a regional commercial hub.
  • 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_69d87f198bc48190a8b7e53ca15b7ead completed April 10, 2026, 4:39 a.m.
NER Named-entity recognition batch_69e184522b2c8190986daae6cb2d9db4 completed April 17, 2026, 12:52 a.m.
NED1 Entity disambiguation (via context triple) batch_69fffeed4e008190b1e8d924b9dc9d37 completed May 10, 2026, 3:43 a.m.
Created at: April 10, 2026, 4:59 a.m.