Triple
T15776539
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tashkent Metro |
E382505
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Chilonzor station
Chilonzor station is a metro station on the Tashkent Metro system in Tashkent, Uzbekistan.
|
E1177994
|
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: Chilonzor station | Statement: [Tashkent Metro, hasStation, Chilonzor station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chilonzor station Context triple: [Tashkent Metro, hasStation, Chilonzor station]
-
A.
Liziba Station
Liziba Station is a famous Chongqing Metro station known for its striking design where trains appear to pass directly through a residential building.
-
B.
Bolna Station
Bolna Station is a remote railway stop on Norway’s Nordland Line, serving the mountainous Saltfjellet region just north of the Arctic Circle.
-
C.
Hankar station
Hankar station is a Brussels Metro station on the city's Line 5, serving the Auderghem municipality in southeastern Brussels.
-
D.
Lesja Station
Lesja Station is a rural railway stop in Norway serving the village of Lesja and connecting the area to the wider national rail network.
-
E.
Kwinana station
Kwinana station is a suburban passenger railway station in Perth, Western Australia, serving the Kwinana area on the Mandurah line.
- 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: Chilonzor station Triple: [Tashkent Metro, hasStation, Chilonzor station]
Generated description
Chilonzor station is a metro station on the Tashkent Metro system in Tashkent, Uzbekistan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Chilonzor station Target entity description: Chilonzor station is a metro station on the Tashkent Metro system in Tashkent, Uzbekistan.
-
A.
Liziba Station
Liziba Station is a famous Chongqing Metro station known for its striking design where trains appear to pass directly through a residential building.
-
B.
Bolna Station
Bolna Station is a remote railway stop on Norway’s Nordland Line, serving the mountainous Saltfjellet region just north of the Arctic Circle.
-
C.
Hankar station
Hankar station is a Brussels Metro station on the city's Line 5, serving the Auderghem municipality in southeastern Brussels.
-
D.
Lesja Station
Lesja Station is a rural railway stop in Norway serving the village of Lesja and connecting the area to the wider national rail network.
-
E.
Kwinana station
Kwinana station is a suburban passenger railway station in Perth, Western Australia, serving the Kwinana area on the Mandurah line.
- 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_69ff998559088190b1f2942564a42ace |
completed | May 9, 2026, 8:31 p.m. |
| NEDg | Description generation | batch_69ff9a56d43c8190819deb48d59e16cb |
completed | May 9, 2026, 8:34 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff9acbd2b481908b9d415e26d0db81 |
completed | May 9, 2026, 8:36 p.m. |
Created at: April 10, 2026, 4:47 a.m.