Triple
T12965330
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monash University Malaysia |
E321246
|
entity |
| Predicate | city |
P40
|
FINISHED |
| Object | Subang Jaya |
E210328
|
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: Subang Jaya | Statement: [Monash University Malaysia, city, Subang Jaya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Subang Jaya Context triple: [Monash University Malaysia, city, Subang Jaya]
-
A.
Subang Jaya
chosen
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.
-
B.
Segambut
Segambut is a parliamentary constituency and township area in Kuala Lumpur, Malaysia, encompassing both residential and institutional landmarks.
-
C.
Jasinga
Jasinga is a district-level area in West Java, Indonesia, known as one of the administrative regions within Bogor Regency.
-
D.
Pandan Jaya
Pandan Jaya is a residential and commercial township located within the municipality of Ampang Jaya in Selangor, Malaysia.
-
E.
Kajang
Kajang is a major town in the Hulu Langat District of Selangor, Malaysia, known for its satay and role as a rapidly developing suburban and commercial hub near Kuala Lumpur.
- 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_69d80763bd6c819094437da5b20b01d2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97e30b7e88190ac07c91147b62d16 |
completed | April 10, 2026, 10:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6c0f4798c8190861638c699e98045 |
completed | May 3, 2026, 3:28 a.m. |
Created at: April 9, 2026, 8:27 p.m.