Triple

T26976286
Position Surface form Disambiguated ID Type / Status
Subject Nishiddho Loban E679466 entity
Predicate fictionalSetting P26457 FINISHED
Object Bangladesh E14165 NE FINISHED

How this triple was built (1 step)

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: Bangladesh | Statement: [Nishiddho Loban, fictionalSetting, Bangladesh]

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_69eeeb507a7081909d516e1fa08b7d29 completed April 27, 2026, 4:51 a.m.
NER Named-entity recognition batch_69f6212856b081909baa2f2083383a48 completed May 2, 2026, 4:07 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12298c5e108190afc4ecf55db87fee completed May 23, 2026, 10:26 p.m.
Created at: April 27, 2026, 6:42 a.m.