Triple

T6301266
Position Surface form Disambiguated ID Type / Status
Subject Betawi Malay E141259 entity
Predicate hasAlternativeName P39 FINISHED
Object Betawi E554120 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: Betawi | Statement: [Betawi Malay, hasAlternativeName, Betawi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Betawi
Context triple: [Betawi Malay, hasAlternativeName, Betawi]
  • A. Betawi chosen
    Betawi is an Austronesian language spoken primarily by the Betawi people in and around Jakarta, Indonesia, and is closely associated with the city's urban culture and history.
  • B. Jatinegara
    Jatinegara is a district in East Jakarta, Indonesia, known as a densely populated urban area with significant transportation hubs and historical sites.
  • C. Bidayuh
    Bidayuh is an indigenous ethnic group of Borneo, primarily in Sarawak, Malaysia, known for its distinct languages, traditional longhouse culture, and rich agricultural and ritual practices.
  • D. Cipinang
    Cipinang is a neighborhood in East Jakarta, Indonesia, known for housing one of the country’s main prisons and various urban residential and commercial areas.
  • E. Subang Jaya
    Subang Jaya is a major suburban city in the Klang Valley region of Malaysia, known for its dense residential areas, commercial hubs, and educational institutions.
  • 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_69c008cf0ad4819095def81e2bd42f9f completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c0645bb41481909294b06e2b3e1845 completed March 22, 2026, 9:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69c5e42f38bc819086a3e66a83ffc792 completed March 27, 2026, 1:58 a.m.
Created at: March 22, 2026, 4:27 p.m.