Triple

T10490484
Position Surface form Disambiguated ID Type / Status
Subject Shabo language E247404 entity
Predicate alternativeName P39 FINISHED
Object Mekeyir E869027 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: Mekeyir | Statement: [Shabo language, alternativeName, Mekeyir]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mekeyir
Context triple: [Shabo language, alternativeName, Mekeyir]
  • A. Mekeyir chosen
    Mekeyir is an alternative name for the Shabo language, a little-documented and endangered language spoken in southwestern Ethiopia.
  • B. Takelsa
    Takelsa is a coastal town in northeastern Tunisia known for its agriculture and location within the Nabeul region on the Cap Bon peninsula.
  • C. Birsay
    Birsay is a coastal parish and village area on the northwest of Orkney Mainland in Scotland, known for its rich Norse history and archaeological sites.
  • D. Tarmuwa
    Tarmuwa is a local government area in northeastern Nigeria known for its predominantly rural communities within Yobe State.
  • E. Khanke
    Khanke is a village in northern Iraq’s Kurdistan Region, known for hosting large camps for internally displaced Yazidis who fled ISIS violence.
  • 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_69d381c309b88190af78aa681cf6a4c2 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d5097d61e08190952d4354ef1bce52 completed April 7, 2026, 1:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69d933c5caa08190a5fba92ebf4b0ff9 completed April 10, 2026, 5:30 p.m.
Created at: April 6, 2026, 12:23 p.m.