Triple
T12575899
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BEM |
E300204
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | MRT subway system |
E990550
|
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: MRT subway system | Statement: [BEM, partOf, MRT subway system]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MRT subway system Context triple: [BEM, partOf, MRT subway system]
-
A.
MRT subway
The MRT subway in Bangkok is a major rapid transit system that provides fast, air-conditioned underground and elevated rail services across key areas of the city.
-
B.
MRT
MRT is the three-letter ISO 3166-1 alpha-3 country code assigned to Mauritania.
-
C.
MRT
MRT is the commonly used abbreviation for the Taipei Metro, the rapid transit system serving Taipei and its surrounding areas.
-
D.
MRT
chosen
MRT is a common abbreviation for urban metro or subway systems providing high-capacity public transportation in major cities.
-
E.
Metro Rail
Metro Rail is the urban rapid transit rail system serving Los Angeles County, providing light rail and subway services across the region.
- 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_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_69f6686395c081909410b429fce6ebf8 |
completed | May 2, 2026, 9:10 p.m. |
Created at: April 9, 2026, 4:47 p.m.