Triple
T15776550
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tashkent Metro |
E382505
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Doʻstlik station
Doʻstlik station is a metro station in Tashkent, Uzbekistan, serving passengers on the city's rapid transit network.
|
E1176334
|
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: Doʻstlik station | Statement: [Tashkent Metro, hasStation, Doʻstlik station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Doʻstlik station Context triple: [Tashkent Metro, hasStation, Doʻstlik station]
-
A.
Khimvolokno station
Khimvolokno station is a stop on the Volgograd Metrotram light rail system in Volgograd, Russia.
-
B.
Gagarina station
Gagarina station is a stop on the Volgograd Metrotram system in Volgograd, Russia, named in honor of cosmonaut Yuri Gagarin.
-
C.
Druzhnaya IV Station
Druzhnaya IV Station is a Russian research facility in Antarctica used for scientific studies in the polar environment.
-
D.
Finlyandsky Station
Finlyandsky Station is a historic railway terminal in Saint Petersburg, Russia, best known as the arrival point of Vladimir Lenin in 1917 before the October Revolution.
-
E.
Shelepikha MCC station
Shelepikha MCC station is a passenger railway station on Moscow’s orbital Moscow Central Circle urban rail line, serving the Shelepikha district with connections to the city’s wider metro network.
- 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: Doʻstlik station Triple: [Tashkent Metro, hasStation, Doʻstlik station]
Generated description
Doʻstlik station is a metro station in Tashkent, Uzbekistan, serving passengers on the city's rapid transit network.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Doʻstlik station Target entity description: Doʻstlik station is a metro station in Tashkent, Uzbekistan, serving passengers on the city's rapid transit network.
-
A.
Khimvolokno station
Khimvolokno station is a stop on the Volgograd Metrotram light rail system in Volgograd, Russia.
-
B.
Gagarina station
Gagarina station is a stop on the Volgograd Metrotram system in Volgograd, Russia, named in honor of cosmonaut Yuri Gagarin.
-
C.
Druzhnaya IV Station
Druzhnaya IV Station is a Russian research facility in Antarctica used for scientific studies in the polar environment.
-
D.
Finlyandsky Station
Finlyandsky Station is a historic railway terminal in Saint Petersburg, Russia, best known as the arrival point of Vladimir Lenin in 1917 before the October Revolution.
-
E.
Shelepikha MCC station
Shelepikha MCC station is a passenger railway station on Moscow’s orbital Moscow Central Circle urban rail line, serving the Shelepikha district with connections to the city’s wider metro network.
- 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_69d86da09a10819082fe9797b23e4664 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e05199cd8881909462462cec34d35a |
completed | April 16, 2026, 3:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff909b467c819097ee87f51d2001da |
completed | May 9, 2026, 7:52 p.m. |
| NEDg | Description generation | batch_69ff9277dc2881908fe0cd70e3d61f3f |
completed | May 9, 2026, 8 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff93745f508190927b79a5debead12 |
completed | May 9, 2026, 8:05 p.m. |
Created at: April 10, 2026, 4:47 a.m.