Triple
T6301296
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Betawi Malay |
E141259
|
entity |
| Predicate | hasDialect |
P4251
|
FINISHED |
| Object | Outer 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: Outer Betawi | Statement: [Betawi Malay, hasDialect, Outer Betawi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Outer Betawi Context triple: [Betawi Malay, hasDialect, Outer 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.
South Jakarta
South Jakarta is a municipality in the southern part of Indonesia’s capital region, known for its upscale residential areas, business districts, and shopping and entertainment centers.
-
C.
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.
-
D.
Biatah Bidayuh
Biatah Bidayuh is an Austronesian language spoken by a subgroup of the Bidayuh people in Sarawak, Malaysia.
-
E.
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.
- 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_69c603fb43688190839af2ffea45df90 |
completed | March 27, 2026, 4:13 a.m. |
Created at: March 22, 2026, 4:27 p.m.