Triple

T13145705
Position Surface form Disambiguated ID Type / Status
Subject Hela Province E312328 entity
Predicate hasLanguage P15 FINISHED
Object Duna language E602086 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: Duna language | Statement: [Hela Province, hasLanguage, Duna language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Duna language
Context triple: [Hela Province, hasLanguage, Duna language]
  • A. Damara language
    The Damara language is a Khoe (Central Khoisan) language spoken primarily by the Damara people of Namibia.
  • B. Duna–Pogaya languages chosen
    The Duna–Pogaya languages are a small subgroup of Papuan languages spoken in the highlands of Papua New Guinea, recognized as part of the larger Trans–New Guinea language family.
  • C. Saraveca language
    The Saraveca language is an extinct Arawakan language once spoken in Bolivia, known from very limited historical documentation.
  • D. Kaera language
    The Kaera language is a Papuan language spoken by a small community on Pantar Island in eastern Indonesia.
  • E. Dusner language
    The Dusner language is a highly endangered Papuan language spoken by a small community in the West Papua region of Indonesia.
  • 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_69d806aabde48190899e13e41659cae5 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d98bcf6d0c819081d078f33e4bdedc completed April 10, 2026, 11:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6eae675508190991ce5902768c45a completed May 3, 2026, 6:27 a.m.
Created at: April 9, 2026, 9:10 p.m.