Triple

T16567815
Position Surface form Disambiguated ID Type / Status
Subject Masa E402506 entity
Predicate hasEthnologueEntry P19233 FINISHED
Object Masa language
The Masa language is a Central Chadic language spoken primarily in parts of Cameroon and Chad by the Masa people.
 E1221457 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: Masa language | Statement: [Masa, hasEthnologueEntry, Masa language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Masa language
Context triple: [Masa, hasEthnologueEntry, Masa language]
  • A. Mawase language
    The Mawase language is a Papuan language spoken in the Eastern Trans-Fly region of southern New Guinea.
  • B. Tela-Masbuar language
    The Tela-Masbuar language is an Austronesian language spoken on the Babar Islands in Indonesia, belonging to the Babar subgroup of the Central–Eastern Malayo-Polynesian languages.
  • C. Mambae language
    The Mambae language is an Austronesian language spoken primarily in East Timor, notable for its role in local identity and traditional culture.
  • D. Mampruli language
    Mampruli is a Gur language spoken primarily by the Mamprusi people in northern Ghana and parts of neighboring West African countries.
  • E. Marsela language
    The Marsela language is an Austronesian language spoken on Marsela Island in the Maluku province of Indonesia.
  • 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: Masa language
Triple: [Masa, hasEthnologueEntry, Masa language]
Generated description
The Masa language is a Central Chadic language spoken primarily in parts of Cameroon and Chad by the Masa people.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Masa language
Target entity description: The Masa language is a Central Chadic language spoken primarily in parts of Cameroon and Chad by the Masa people.
  • A. Mawase language
    The Mawase language is a Papuan language spoken in the Eastern Trans-Fly region of southern New Guinea.
  • B. Tela-Masbuar language
    The Tela-Masbuar language is an Austronesian language spoken on the Babar Islands in Indonesia, belonging to the Babar subgroup of the Central–Eastern Malayo-Polynesian languages.
  • C. Mambae language
    The Mambae language is an Austronesian language spoken primarily in East Timor, notable for its role in local identity and traditional culture.
  • D. Mampruli language
    Mampruli is a Gur language spoken primarily by the Mamprusi people in northern Ghana and parts of neighboring West African countries.
  • E. Marsela language
    The Marsela language is an Austronesian language spoken on Marsela Island in the Maluku province of Indonesia.
  • 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_69d8838648088190acf97ef11fc3f61b completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e35772f6608190a125c7d3c199c3e2 completed April 18, 2026, 10:05 a.m.
NED1 Entity disambiguation (via context triple) batch_6a006ee3dcbc819087ea66b262585232 completed May 10, 2026, 11:41 a.m.
NEDg Description generation batch_6a006ff5bdb88190be90d7446e24b61f completed May 10, 2026, 11:45 a.m.
NED2 Entity disambiguation (via description) batch_6a007088fd988190b3dfef081769d03e completed May 10, 2026, 11:48 a.m.
Created at: April 10, 2026, 5:16 a.m.