Triple

T6688526
Position Surface form Disambiguated ID Type / Status
Subject Mandar language E152161 entity
Predicate hasDialect P4251 FINISHED
Object Pamboang dialect E610785 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: Pamboang dialect | Statement: [Mandar language, hasDialect, Pamboang dialect]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pamboang dialect
Context triple: [Mandar language, hasDialect, Pamboang dialect]
  • A. Bangang dialect
    The Bangang dialect is a regional variety of the Ghomalaʼ language spoken by the Bangang community in western Cameroon.
  • B. Tigapanah dialect
    The Tigapanah dialect is a regional variety of the Karo Batak language spoken by Karo communities in and around the Tigapanah area of North Sumatra, Indonesia.
  • C. Nabua dialect
    The Nabua dialect is a local variety of the Rinconada Bikol language spoken primarily in and around the municipality of Nabua in Camarines Sur, Philippines.
  • D. Balanipa dialect chosen
    The Balanipa dialect is a regional variety of the Mandar language spoken by communities in the Balanipa area of West Sulawesi, Indonesia.
  • E. Kamia dialect
    The Kamia dialect is a regional variety of the Ipai-Tipai language traditionally spoken by the Kamia (Kumeyaay) people of southern California and northern Baja California.
  • 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_69c687f9977c819097e7f5ada4fe522e completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6b14feb28819097bc157df8a2f96e completed March 27, 2026, 4:33 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7007ad59c8190a752d9b1152c3435 completed March 27, 2026, 10:11 p.m.
Created at: March 27, 2026, 2:04 p.m.