Triple

T16798884
Position Surface form Disambiguated ID Type / Status
Subject Sachal Sarmast E408302 entity
Predicate languageOfWorkOrName P15 FINISHED
Object Seraiki E41867 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: Seraiki | Statement: [Sachal Sarmast, languageOfWorkOrName, Seraiki]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Seraiki
Context triple: [Sachal Sarmast, languageOfWorkOrName, Seraiki]
  • A. Seraiki chosen
    Seraiki is an Indo-Aryan language spoken primarily in central and southern Pakistan, especially in the southern Punjab region.
  • B. Gorani
    Gorani are a Slavic Muslim ethnic group native to the mountainous Gora region spanning parts of Kosovo, Albania, and North Macedonia, known for their distinct dialect and cultural traditions.
  • C. Gorani
    Gorani is a Northwestern Iranian language variety traditionally spoken by Kurdish communities in parts of Iran and Iraq, notable for its rich literary and religious heritage.
  • D. Siwi
    Siwi is a Berber language spoken primarily in Egypt’s Siwa Oasis, known for its unique features and relative isolation from other Berber varieties.
  • E. Tatarbunary
    Tatarbunary is a small town in the historical Budjak region of southwestern Ukraine, known for its agricultural surroundings and multiethnic local culture.
  • 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_69d88393905081908d00a86b99996ac8 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e3b2abc430819080c1303eded5f416 completed April 18, 2026, 4:34 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00ab1299ac81908e9f1eebc3424bb9 completed May 10, 2026, 3:58 p.m.
Created at: April 10, 2026, 5:22 a.m.