Triple
T11087294
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MRT Kajang Line |
E262154
|
entity |
| Predicate | depot |
P14646
|
FINISHED |
| Object |
Kajang Depot
Kajang Depot is a major maintenance and operations facility serving Malaysia’s MRT Kajang Line in the Greater Kuala Lumpur area.
|
E905293
|
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: Kajang Depot | Statement: [MRT Kajang Line, depot, Kajang Depot]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kajang Depot Context triple: [MRT Kajang Line, depot, Kajang Depot]
-
A.
Ampang Depot
Ampang Depot is a maintenance and storage facility serving trains on Kuala Lumpur’s Ampang light rail transit line.
-
B.
Sungai Buloh Depot
Sungai Buloh Depot is a major rail maintenance and operations facility serving Malaysia’s MRT Kajang Line in the Sungai Buloh area of Selangor.
-
C.
Kelana Jaya Depot
Kelana Jaya Depot is a major rail maintenance and storage facility serving Kuala Lumpur’s light rail transit network.
-
D.
Subang Depot
Subang Depot is a maintenance and storage facility serving trains on the LRT Kelana Jaya Line in the Klang Valley rail network of Malaysia.
-
E.
Munyang Depot
Munyang Depot is a maintenance and storage facility serving the Daegu Metro system in Daegu, South Korea.
- 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: Kajang Depot Triple: [MRT Kajang Line, depot, Kajang Depot]
Generated description
Kajang Depot is a major maintenance and operations facility serving Malaysia’s MRT Kajang Line in the Greater Kuala Lumpur area.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kajang Depot Target entity description: Kajang Depot is a major maintenance and operations facility serving Malaysia’s MRT Kajang Line in the Greater Kuala Lumpur area.
-
A.
Ampang Depot
Ampang Depot is a maintenance and storage facility serving trains on Kuala Lumpur’s Ampang light rail transit line.
-
B.
Sungai Buloh Depot
Sungai Buloh Depot is a major rail maintenance and operations facility serving Malaysia’s MRT Kajang Line in the Sungai Buloh area of Selangor.
-
C.
Kelana Jaya Depot
Kelana Jaya Depot is a major rail maintenance and storage facility serving Kuala Lumpur’s light rail transit network.
-
D.
Subang Depot
Subang Depot is a maintenance and storage facility serving trains on the LRT Kelana Jaya Line in the Klang Valley rail network of Malaysia.
-
E.
Munyang Depot
Munyang Depot is a maintenance and storage facility serving the Daegu Metro system in Daegu, South Korea.
- 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_69d6aa9983c08190b0ef61603b69feac |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d799c5008081908f59612243fa4f7a |
completed | April 9, 2026, 12:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e42d66ded88190877a20a10f012d6b |
completed | April 19, 2026, 1:18 a.m. |
| NEDg | Description generation | batch_69e42e1daa3c8190b598adcf9bac00f3 |
completed | April 19, 2026, 1:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e42f415b1081909f9eedcb3640cdc3 |
completed | April 19, 2026, 1:26 a.m. |
Created at: April 8, 2026, 9:27 p.m.