Triple

T11093571
Position Surface form Disambiguated ID Type / Status
Subject Sara languages E262315 entity
Predicate hasMember P10 FINISHED
Object Zande languages E272630 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: Zande languages | Statement: [Sara languages, hasMember, Zande languages]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Zande languages
Context triple: [Sara languages, hasMember, Zande languages]
  • A. Zande language chosen
    The Zande language is a Central African language spoken primarily by the Azande people across parts of South Sudan, the Central African Republic, and the Democratic Republic of the Congo.
  • B. Ngemba languages
    The Ngemba languages are a group of closely related Bantoid languages spoken primarily in the Grassfields region of Cameroon.
  • C. Tsogo languages
    The Tsogo languages are a small group of closely related Bantu languages spoken primarily in Gabon in Central Africa.
  • D. Teke–Mbede languages
    The Teke–Mbede languages are a group of closely related Bantu languages spoken primarily in Gabon and neighboring Central African countries.
  • E. Grassfields languages
    Grassfields languages are a group of closely related Southern Bantoid languages spoken primarily in the Grassfields region of western Cameroon.
  • 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_69d6aa9a40d88190a373e2c7e48285db completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d799ed12d88190a4ad8c346d68f11f completed April 9, 2026, 12:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69e441bb14d08190ac01bf3daa34ae43 completed April 19, 2026, 2:45 a.m.
Created at: April 8, 2026, 9:27 p.m.