Triple

T36251791
Position Surface form Disambiguated ID Type / Status
Subject Macro-Jê hypothesis E891821 entity
Predicate proposesGeneticUnitOf P176567 FINISHED
Object Fulniô language
The Fulniô language is an indigenous language of northeastern Brazil, spoken by the Fulniô people and notable for its uncertain classification and potential, but debated, connection to the Macro-Jê language family.
E2174814 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: Fulniô language | Statement: [Macro-Jê hypothesis, proposesGeneticUnitOf, Fulniô language]
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: Fulniô language
Triple: [Macro-Jê hypothesis, proposesGeneticUnitOf, Fulniô language]
Generated description
The Fulniô language is an indigenous language of northeastern Brazil, spoken by the Fulniô people and notable for its uncertain classification and potential, but debated, connection to the Macro-Jê language family.

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_69f76e4599108190811532e707d6bc2c completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7b9a71ed48190bb9377c56de3e02c completed May 3, 2026, 9:09 p.m.
NED1 Entity disambiguation (via context triple) batch_6a394d4c310c8190986263c4158496b4 completed June 22, 2026, 2:57 p.m.
NEDg Description generation batch_6a394f1e98988190b1a44c79d95e6c0b completed June 22, 2026, 3:05 p.m.
NED2 Entity disambiguation (via description) batch_6a394f9b84008190b401b1484aaaebaa completed June 22, 2026, 3:07 p.m.
Created at: May 3, 2026, 4:09 p.m.