Triple
T10681407
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Metrovagonmash |
E251763
|
entity |
| Predicate | servesTransportSystem |
P14525
|
FINISHED |
| Object | Baku Metro |
E382528
|
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: Baku Metro | Statement: [Metrovagonmash, servesTransportSystem, Baku Metro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Baku Metro Context triple: [Metrovagonmash, servesTransportSystem, Baku Metro]
-
A.
Baku Metro
chosen
Baku Metro is the rapid transit system serving Azerbaijan’s capital city, known for its Soviet-era architecture and role as a key component of Baku’s urban transportation network.
-
B.
Tbilisi Metro
Tbilisi Metro is the rapid transit system serving Georgia’s capital city, providing a primary backbone for urban public transportation.
-
C.
Yerevan Metro
Yerevan Metro is the rapid transit system serving Armenia’s capital city, providing underground rail transportation across key urban areas.
-
D.
Tashkent Metro
Tashkent Metro is the rapid transit system serving Uzbekistan’s capital, notable for its Soviet-era architecture and ornately decorated underground stations.
-
E.
Ürümqi Metro
Ürümqi Metro is the rapid transit system serving Ürümqi, the capital of China’s Xinjiang Uyghur Autonomous 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_69d6aa5bd7c08190a816e733b4045c23 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6fcc30be481909922844b539b622d |
completed | April 9, 2026, 1:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d98885abf88190b54ed9db779d3ff0 |
completed | April 10, 2026, 11:32 p.m. |
Created at: April 8, 2026, 9:10 p.m.