Triple
T23080327
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ürümqi Metro Line 1 |
E575450
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Ürümqi Metro |
—
|
NE NERFINISHED |
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: Ürümqi Metro | Statement: [Ürümqi Metro Line 1, partOf, Ürümqi Metro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ürümqi Metro Context triple: [Ürümqi Metro Line 1, partOf, Ürümqi Metro]
-
A.
Ürümqi Metro
chosen
Ürümqi Metro is the rapid transit system serving Ürümqi, the capital of China’s Xinjiang Uyghur Autonomous Region.
-
B.
Tashkent Metro
Tashkent Metro is the rapid transit system serving Uzbekistan’s capital, notable for its Soviet-era architecture and ornately decorated underground stations.
-
C.
Almaty Metro
Almaty Metro is the rapid transit system serving Kazakhstan’s largest city, featuring underground lines that provide urban public transportation.
-
D.
Kazan Metro
Kazan Metro is the rapid transit system serving the city of Kazan, Russia, providing urban rail transportation across several key districts.
-
E.
Novosibirsk Metro
Novosibirsk Metro is a rapid transit system in Novosibirsk, Russia, serving as a key component of the city's public transportation network with several lines and stations across the urban area.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245be28d48190ad1348d5a73db37d |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18c66a80481909ebc2ba69f1e4bd9 |
completed | April 29, 2026, 4:43 a.m. |
Created at: April 17, 2026, 3:56 p.m.