Triple
T1352022
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Malayic languages |
E28902
|
entity |
| Predicate | hasMember |
P10
|
FINISHED |
| Object | Jakartan Malay |
E141259
|
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: Jakartan Malay | Statement: [Malayic languages, hasMember, Jakartan Malay]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jakartan Malay Context triple: [Malayic languages, hasMember, Jakartan Malay]
-
A.
Betawi Malay
chosen
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
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.
Kupang Malay
Kupang Malay is a Malay-based creole language spoken primarily in and around the city of Kupang in eastern Indonesia.
-
D.
Papuan Malay
Papuan Malay is an eastern Indonesian variety of Malay used as a lingua franca in Papua, characterized by distinctive phonological and grammatical features influenced by local Papuan languages.
-
E.
MALAYSIAN
MALAYSIAN is the radio callsign used by Malaysia Airlines for its commercial flight operations.
- 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_69acd47917d88190a13d7705f09c0b58 |
completed | March 8, 2026, 1:44 a.m. |
Created at: March 1, 2026, 7:56 p.m.