Triple
T8739747
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pasir Mas |
E207470
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object |
Rantau Panjang
Rantau Panjang is a Malaysian border town in Kelantan known for its bustling cross-border trade and duty-free shopping near the Thailand border.
|
E758044
|
NE FINISHED |
How this triple was built (4 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: Rantau Panjang | Statement: [Pasir Mas, locatedNear, Rantau Panjang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rantau Panjang Context triple: [Pasir Mas, locatedNear, Rantau Panjang]
-
A.
Rantau
Rantau is a state constituency in Malaysia’s Negeri Sembilan state, represented in the Negeri Sembilan State Legislative Assembly.
-
B.
Kuala Kangsar
Kuala Kangsar is a historic royal town in the Malaysian state of Perak, known as the traditional seat of the Perak Sultanate.
-
C.
Lumut
Lumut is a coastal town in the Malaysian state of Perak, known as a gateway to Pangkor Island and as a naval and port town.
-
D.
Lumut
Lumut is a small island located within Indonesia’s Bangka Belitung Islands province, known for its coastal tropical setting.
-
E.
Seremban
Seremban is the capital city of the Malaysian state of Negeri Sembilan, known as an administrative, commercial, and cultural center in the western part of Peninsular Malaysia.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Rantau Panjang Triple: [Pasir Mas, locatedNear, Rantau Panjang]
Generated description
Rantau Panjang is a Malaysian border town in Kelantan known for its bustling cross-border trade and duty-free shopping near the Thailand border.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rantau Panjang Target entity description: Rantau Panjang is a Malaysian border town in Kelantan known for its bustling cross-border trade and duty-free shopping near the Thailand border.
-
A.
Rantau
Rantau is a state constituency in Malaysia’s Negeri Sembilan state, represented in the Negeri Sembilan State Legislative Assembly.
-
B.
Kuala Kangsar
Kuala Kangsar is a historic royal town in the Malaysian state of Perak, known as the traditional seat of the Perak Sultanate.
-
C.
Lumut
Lumut is a coastal town in the Malaysian state of Perak, known as a gateway to Pangkor Island and as a naval and port town.
-
D.
Lumut
Lumut is a small island located within Indonesia’s Bangka Belitung Islands province, known for its coastal tropical setting.
-
E.
Seremban
Seremban is the capital city of the Malaysian state of Negeri Sembilan, known as an administrative, commercial, and cultural center in the western part of Peninsular Malaysia.
- F. None of above. chosen
Provenance (5 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_69ca835a03a081909d4d4cd01a18c9fb |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5d486e34819094a6c6ec26c047cf |
completed | March 31, 2026, 11:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf6ef8d7f88190aea21c82da47e4a0 |
completed | April 3, 2026, 7:40 a.m. |
| NEDg | Description generation | batch_69cf70c981808190856827fbcd4c4671 |
completed | April 3, 2026, 7:48 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cf71914ec48190bd623d8d773e7ca7 |
completed | April 3, 2026, 7:51 a.m. |
Created at: March 30, 2026, 6:38 p.m.