Triple

T2446356
Position Surface form Disambiguated ID Type / Status
Subject Kikuyu E53602 entity
Predicate closelyRelatedTo P37 FINISHED
Object Meru language
The Meru language is a Bantu language spoken by the Meru people of central Kenya, closely related to Kikuyu and other Mount Kenya languages.
 E267657 NE FINISHED

How this triple was built (4 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: Meru language | Statement: [Kikuyu, closelyRelatedTo, Meru language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Meru language
Context triple: [Kikuyu, closelyRelatedTo, Meru language]
  • A. Murle language
    The Murle language is an Eastern Sudanic language spoken primarily by the Murle people of South Sudan.
  • B. Moru language
    The Moru language is a Central Sudanic language spoken primarily by the Moru people of South Sudan.
  • C. Baliledu language
    The Baliledu language is an Austronesian language of the Bima–Sumba subgroup spoken by a local community in eastern Indonesia.
  • D. Muna language
    The Muna language is an Austronesian language spoken primarily on Muna Island in Southeast Sulawesi, Indonesia, known for its rich verbal morphology and distinct phonological system.
  • E. Bagirmi language
    The Bagirmi language is a Central Sudanic language spoken primarily in Chad by the Bagirmi people, known for its role as a regional lingua franca and its rich oral tradition.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Meru language
Triple: [Kikuyu, closelyRelatedTo, Meru language]
Generated description
The Meru language is a Bantu language spoken by the Meru people of central Kenya, closely related to Kikuyu and other Mount Kenya languages.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Meru language
Target entity description: The Meru language is a Bantu language spoken by the Meru people of central Kenya, closely related to Kikuyu and other Mount Kenya languages.
  • A. Murle language
    The Murle language is an Eastern Sudanic language spoken primarily by the Murle people of South Sudan.
  • B. Moru language
    The Moru language is a Central Sudanic language spoken primarily by the Moru people of South Sudan.
  • C. Baliledu language
    The Baliledu language is an Austronesian language of the Bima–Sumba subgroup spoken by a local community in eastern Indonesia.
  • D. Muna language
    The Muna language is an Austronesian language spoken primarily on Muna Island in Southeast Sulawesi, Indonesia, known for its rich verbal morphology and distinct phonological system.
  • E. Bagirmi language
    The Bagirmi language is a Central Sudanic language spoken primarily in Chad by the Bagirmi people, known for its role as a regional lingua franca and its rich oral tradition.
  • F. None of above. chosen

Provenance (5 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_69ab495d227c8190b26ae6548eeb1019 completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abca25a84c8190859bf51000beffec completed March 7, 2026, 6:48 a.m.
NED1 Entity disambiguation (via context triple) batch_69aef0bd7a088190b635a8bac233c5cd completed March 9, 2026, 4:09 p.m.
NEDg Description generation batch_69aef50bc7ac8190add8ee63c5621dc1 completed March 9, 2026, 4:27 p.m.
NED2 Entity disambiguation (via description) batch_69aef632e2e08190b21023cbb0f12be8 completed March 9, 2026, 4:32 p.m.
Created at: March 6, 2026, 9:43 p.m.