Triple
T8420126
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Port Klang |
E198826
|
entity |
| Predicate | nearbyCity |
P350
|
FINISHED |
| Object | Petaling Jaya |
E266700
|
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: Petaling Jaya | Statement: [Port Klang, nearbyCity, Petaling Jaya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Petaling Jaya Context triple: [Port Klang, nearbyCity, Petaling Jaya]
-
A.
Petaling Jaya
chosen
Petaling Jaya is a major city in the state of Selangor, Malaysia, known as a key commercial and residential hub adjacent to Kuala Lumpur.
-
B.
Shah Alam
Shah Alam is a planned city in Malaysia known as the administrative and commercial center of the state of Selangor.
-
C.
Cyberjaya
Cyberjaya is a planned smart city in Malaysia known as a major technology and innovation hub, hosting numerous IT companies, startups, and educational institutions.
-
D.
Putrajaya
Putrajaya is Malaysia’s planned federal administrative capital, known for its modern architecture, landscaped boulevards, and numerous government complexes.
-
E.
Alor Setar
Alor Setar is a major city in northwestern Peninsular Malaysia known as an administrative, cultural, and commercial hub near the border with Thailand.
- 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_69ca8312d63c8190bf133b676b44a385 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cb84c941988190884a5c0cbb44bcc2 |
completed | March 31, 2026, 8:24 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce3988932c8190969c4c0295348636 |
completed | April 2, 2026, 9:40 a.m. |
Created at: March 30, 2026, 6:06 p.m.