Triple

T4866322
Position Surface form Disambiguated ID Type / Status
Subject Kunama E108778 entity
Predicate hasDialects P4251 FINISHED
Object Ilit Kunama E108778 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: Ilit Kunama | Statement: [Kunama, hasDialects, Ilit Kunama]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ilit Kunama
Context triple: [Kunama, hasDialects, Ilit Kunama]
  • A. Manjil
    Manjil is a town in northern Iran known for its strong winds and wind farms, situated in the mountainous region of Gilan Province.
  • B. Iya Labunka
    Iya Labunka is a film producer known for her work on genre films, including serving as a producer on the horror sequel "Scream 4."
  • C. Kunama chosen
    Kunama is a Nilo-Saharan language spoken primarily by the Kunama people in western Eritrea and adjacent parts of Ethiopia.
  • D. Kala Lagaw Ya
    Kala Lagaw Ya is an Australian Aboriginal language of the Western Torres Strait, spoken by Torres Strait Islanders and known for its complex grammar and long-standing contact with Papuan and Austronesian languages.
  • E. Kulitan
    Kulitan is the indigenous precolonial script used to write the Kapampangan language of the Philippines.
  • 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_69bd440b965081908b0557721cae6338 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd6d7a42f88190bb1ef7261bcbc2a8 completed March 20, 2026, 3:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69be67e5d96c8190b2a509d9fb81211a completed March 21, 2026, 9:41 a.m.
Created at: March 20, 2026, 1:26 p.m.