Triple

T452069
Position Surface form Disambiguated ID Type / Status
Subject Mother Teresa E7150 entity
Predicate languageSpoken P151 FINISHED
Object Bengali E5055 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: Bengali | Statement: [Mother Teresa, languageSpoken, Bengali]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bengali
Context triple: [Mother Teresa, languageSpoken, Bengali]
  • A. Bengali chosen
    Bengali is an Indo-Aryan language spoken primarily in the Bengal region of South Asia and serving as the official and most widely used language of Bangladesh and the Indian state of West Bengal.
  • B. Bhojpuri
    Bhojpuri is an Indo-Aryan language spoken primarily in eastern Uttar Pradesh, western Bihar, and parts of Nepal, with a rich folk culture and a large diaspora community.
  • C. Hindi
    Hindi is an Indo-Aryan language widely spoken across northern and central India and used in government, education, media, and popular culture.
  • D. Bengali script
    Bengali script is an abugida used across eastern South Asia to write languages such as Bengali and Assamese, derived from the ancient Brahmi script.
  • E. Odia
    Odia is an Indo-Aryan language spoken primarily in the Indian state of Odisha, known for its rich literary tradition and classical language status.
  • 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_69a2e7e4676c81909ea0dbdecac0687c completed Feb. 28, 2026, 1:04 p.m.
NER Named-entity recognition batch_69a2ef854f7481909dc2207faf0327ec completed Feb. 28, 2026, 1:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69a44802e858819081a0b5b98bb25bce completed March 1, 2026, 2:06 p.m.
Created at: Feb. 28, 2026, 1:12 p.m.