Triple

T1352015
Position Surface form Disambiguated ID Type / Status
Subject Malayic languages E28902 entity
Predicate hasMember P10 FINISHED
Object Standard Malay E23976 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: Standard Malay | Statement: [Malayic languages, hasMember, Standard Malay]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Standard Malay
Context triple: [Malayic languages, hasMember, Standard Malay]
  • A. Betawi Malay
    Betawi Malay is a Malay-based creole language spoken primarily in Jakarta, Indonesia, serving as the traditional language of the Betawi ethnic community.
  • B. Malay chosen
    Malay is an Austronesian language widely spoken in Southeast Asia and serves as a national or official language in several countries, including Malaysia, Indonesia (as Indonesian), Brunei, and Singapore.
  • C. MALAYSIAN
    MALAYSIAN is the radio callsign used by Malaysia Airlines for its commercial flight operations.
  • D. Old Malay
    Old Malay is an early historical form of the Malay language that served as a major lingua franca and literary language in maritime Southeast Asia.
  • E. Standard English
    Standard English is the widely accepted, codified form of the English language used in formal writing, education, and public communication across English-speaking countries.
  • 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_69a498571d248190a0ac9eb02d97097f completed March 1, 2026, 7:49 p.m.
NER Named-entity recognition batch_69a4c26b1b4881908ae4b1b2c9b268a0 completed March 1, 2026, 10:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69acc63eef908190aef058396f63a5a4 completed March 8, 2026, 12:43 a.m.
Created at: March 1, 2026, 7:56 p.m.