Triple
T12576031
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MRT Blue Line |
E300207
|
entity |
| Predicate | depot |
P14646
|
FINISHED |
| Object |
Huai Khwang depot
Huai Khwang depot is a maintenance and storage facility serving Bangkok’s MRT Blue Line rapid transit system.
|
E990552
|
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: Huai Khwang depot | Statement: [MRT Blue Line, depot, Huai Khwang depot]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Huai Khwang depot Context triple: [MRT Blue Line, depot, Huai Khwang depot]
-
A.
Phaya Thai station
Phaya Thai station is a major transit hub in Bangkok that connects the Airport Rail Link with the city’s urban rail network.
-
B.
Mo Chit Depot
Mo Chit Depot is a major maintenance and stabling facility serving Bangkok’s BTS Skytrain system.
-
C.
Lat Krabang station
Lat Krabang station is a railway station in Bangkok that serves the Airport Rail Link line connecting the city to Suvarnabhumi Airport.
-
D.
Ratchaprarop station
Ratchaprarop station is an elevated railway station in central Bangkok serving the Airport Rail Link line that connects the city to Suvarnabhumi Airport.
-
E.
Sala Daeng Station
Sala Daeng Station is a major elevated BTS Skytrain station in central Bangkok, serving as a key transit hub near the Silom business and entertainment district.
- 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: Huai Khwang depot Triple: [MRT Blue Line, depot, Huai Khwang depot]
Generated description
Huai Khwang depot is a maintenance and storage facility serving Bangkok’s MRT Blue Line rapid transit system.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Huai Khwang depot Target entity description: Huai Khwang depot is a maintenance and storage facility serving Bangkok’s MRT Blue Line rapid transit system.
-
A.
Phaya Thai station
Phaya Thai station is a major transit hub in Bangkok that connects the Airport Rail Link with the city’s urban rail network.
-
B.
Mo Chit Depot
Mo Chit Depot is a major maintenance and stabling facility serving Bangkok’s BTS Skytrain system.
-
C.
Lat Krabang station
Lat Krabang station is a railway station in Bangkok that serves the Airport Rail Link line connecting the city to Suvarnabhumi Airport.
-
D.
Ratchaprarop station
Ratchaprarop station is an elevated railway station in central Bangkok serving the Airport Rail Link line that connects the city to Suvarnabhumi Airport.
-
E.
Sala Daeng Station
Sala Daeng Station is a major elevated BTS Skytrain station in central Bangkok, serving as a key transit hub near the Silom business and entertainment district.
- 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_69d7bde87b648190bcd0266e9efde098 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d954a629fc8190a1c3b6777aad4527 |
completed | April 10, 2026, 7:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f65597dc70819089ddc1794e9bd1b7 |
completed | May 2, 2026, 7:50 p.m. |
| NEDg | Description generation | batch_69f656f812bc8190a2a691285fc30e03 |
completed | May 2, 2026, 7:56 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f657ea0c6c8190992a0101904e92f2 |
completed | May 2, 2026, 8 p.m. |
Created at: April 9, 2026, 4:47 p.m.