Triple

T5635791
Position Surface form Disambiguated ID Type / Status
Subject Toqabaqita E147944 entity
Predicate closelyRelatedTo P37 FINISHED
Object Fataleka language E157637 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: Fataleka language | Statement: [Toqabaqita, closelyRelatedTo, Fataleka language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Fataleka language
Context triple: [Toqabaqita, closelyRelatedTo, Fataleka language]
  • A. Fataleka language chosen
    The Fataleka language is an Austronesian language spoken by the Fataleka people on Malaita in the Solomon Islands.
  • B. Baliledu language
    The Baliledu language is an Austronesian language of the Bima–Sumba subgroup spoken by a local community in eastern Indonesia.
  • C. Murle language
    The Murle language is an Eastern Sudanic language spoken primarily by the Murle people of South Sudan.
  • D. Defaka language
    The Defaka language is a highly endangered Niger-Congo language spoken by a small community in Nigeria’s Niger Delta region.
  • E. Sanglechi language
    The Sanglechi language is an Eastern Iranian language spoken by a small community in the Sanglech Valley region of Afghanistan and Tajikistan.
  • 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_69c00907bc8881909ed760d3ed73ef35 completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c0226286208190b6ccf036cc09fe82 completed March 22, 2026, 5:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69c04d6d2dfc8190a2eabb8beda04ee5 completed March 22, 2026, 8:13 p.m.
Created at: March 22, 2026, 3:41 p.m.