Triple

T7197747
Position Surface form Disambiguated ID Type / Status
Subject Tontemboan language E168657 entity
Predicate hasGlottologName P6521 FINISHED
Object Tontemboan E648247 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: Tontemboan | Statement: [Tontemboan language, hasGlottologName, Tontemboan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tontemboan
Context triple: [Tontemboan language, hasGlottologName, Tontemboan]
  • A. Tontemboan chosen
    Tontemboan is an Austronesian language spoken by the Tontemboan people in North Sulawesi, Indonesia.
  • B. Abong-Mbang
    Abong-Mbang is a town in eastern Cameroon that serves as a local administrative and commercial center in the East Region.
  • C. Dutsin-Ma
    Dutsin-Ma is a town in northern Nigeria known for hosting the Federal University Dutsin-Ma and serving as an important local commercial and educational center.
  • D. Rumuokoro
    Rumuokoro is a bustling urban town and major commercial transport hub in Obio-Akpor, within the Port Harcourt metropolitan area of Rivers State, Nigeria.
  • E. Benina
    Benina is a town in eastern Libya that serves as the main gateway to the nearby city of Benghazi through its international airport.
  • 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_69c68a5376748190bb500f03df86e93e completed March 27, 2026, 1:46 p.m.
NER Named-entity recognition batch_69c6e92a5d288190955f703470e75bf3 completed March 27, 2026, 8:31 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7cbe7d42c8190915fd0713523cbb0 completed March 28, 2026, 12:39 p.m.
Created at: March 27, 2026, 2:52 p.m.