Triple

T16184619
Position Surface form Disambiguated ID Type / Status
Subject Burúśaski E392767 entity
Predicate hasDialect P4251 FINISHED
Object Hunza dialect E392765 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: Hunza dialect | Statement: [Burúśaski, hasDialect, Hunza dialect]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hunza dialect
Context triple: [Burúśaski, hasDialect, Hunza dialect]
  • A. Hunza dialect chosen
    The Hunza dialect is a regional variety of the Burushaski language spoken primarily in the Hunza Valley of northern Pakistan.
  • B. Harauti dialect
    The Harauti dialect is an Indo-Aryan variety spoken primarily in the Hadoti (Harauti) region of Rajasthan and neighboring areas of India.
  • C. Anghan dialect
    The Anghan dialect is a regional variety of the Tyap language spoken by the Anghan (Kamantan) people of central Nigeria.
  • D. Alar-Tunka dialect
    The Alar-Tunka dialect is a regional variety of the Buryat language spoken primarily in the Alar and Tunka areas of Siberia.
  • E. Nankani dialect
    The Nankani dialect is a regional variety of the Gur-language cluster spoken in northern Ghana and southern Burkina Faso, associated with the Nankani people.
  • 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_69d87f1e49ac8190a311b54d32990576 completed April 10, 2026, 4:39 a.m.
NER Named-entity recognition batch_69e2205fc080819097858f36253fef7c completed April 17, 2026, 11:58 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffff0550b48190ac84946b7254552b completed May 10, 2026, 3:44 a.m.
Created at: April 10, 2026, 5:02 a.m.