Triple

T6753774
Position Surface form Disambiguated ID Type / Status
Subject Sara language E154401 entity
Predicate hasDialect P4251 FINISHED
Object Laka
Laka is a dialect of the Sara language spoken in parts of Central Africa, particularly in Chad and neighboring regions.
 E618612 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: Laka | Statement: [Sara language, hasDialect, Laka]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Laka
Context triple: [Sara language, hasDialect, Laka]
  • A. Lokachi
    Lokachi is a small town in western Ukraine situated within the historic and predominantly rural Volyn region.
  • B. Kuanua
    Kuanua is an Austronesian language spoken primarily by the Tolai people of East New Britain in Papua New Guinea.
  • C. Nolana
    Nolana is a genus of flowering plants native mainly to coastal regions of South America, known for their showy, often blue, funnel-shaped blossoms.
  • D. Yelinda
    Yelinda is a dialect of the Bulu language spoken by a specific subgroup of Bulu speakers in Cameroon.
  • E. Marangona
    Marangona is the largest and most famous bell of St Mark's Campanile in Venice, traditionally used to mark the beginning and end of the working day and to signal important civic events.
  • 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: Laka
Triple: [Sara language, hasDialect, Laka]
Generated description
Laka is a dialect of the Sara language spoken in parts of Central Africa, particularly in Chad and neighboring regions.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Laka
Target entity description: Laka is a dialect of the Sara language spoken in parts of Central Africa, particularly in Chad and neighboring regions.
  • A. Lokachi
    Lokachi is a small town in western Ukraine situated within the historic and predominantly rural Volyn region.
  • B. Kuanua
    Kuanua is an Austronesian language spoken primarily by the Tolai people of East New Britain in Papua New Guinea.
  • C. Nolana
    Nolana is a genus of flowering plants native mainly to coastal regions of South America, known for their showy, often blue, funnel-shaped blossoms.
  • D. Yelinda
    Yelinda is a dialect of the Bulu language spoken by a specific subgroup of Bulu speakers in Cameroon.
  • E. Marangona
    Marangona is the largest and most famous bell of St Mark's Campanile in Venice, traditionally used to mark the beginning and end of the working day and to signal important civic events.
  • 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_69c6880fd5808190be684854081e27dd completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d1f32fa08190bb23dc24fef14c8d completed March 27, 2026, 6:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69c712a793cc8190b838806151851711 completed March 27, 2026, 11:28 p.m.
NEDg Description generation batch_69c7132017a881909a8f4a8d4635d53f completed March 27, 2026, 11:30 p.m.
NED2 Entity disambiguation (via description) batch_69c715cc0c9c8190aae641eaffa5bd7b completed March 27, 2026, 11:42 p.m.
Created at: March 27, 2026, 2:11 p.m.