Triple

T623394
Position Surface form Disambiguated ID Type / Status
Subject Saraiki E14560 entity
Predicate hasAlternateName P39 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: [Saraiki, hasAlternateName, Seraiki]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Seraiki
Context triple: [Saraiki, hasAlternateName, 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 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.
  • C. 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.
  • D. Kurmanji
    Kurmanji is the most widely spoken dialect of the Kurdish language, used primarily by Kurds across Turkey, Syria, Iraq, Iran, and the diaspora.
  • E. Hazaragi
    Hazaragi is a variety of Persian primarily spoken by the Hazara people of central Afghanistan and surrounding regions, distinguished by its unique phonology and significant Turkic and Mongolic influences.
  • 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_69a4934b17c881909ace8270e8ddd202 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a49e41753881909f0faed720cc31bc completed March 1, 2026, 8:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69a567012e9c81909d502e29fff35750 completed March 2, 2026, 10:31 a.m.
Created at: March 1, 2026, 7:35 p.m.