Triple
T15776536
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tashkent Metro |
E382505
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Paxtakor station
Paxtakor station is a metro station in the Tashkent Metro system in Tashkent, Uzbekistan.
|
E1176330
|
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: Paxtakor station | Statement: [Tashkent Metro, hasStation, Paxtakor station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paxtakor station Context triple: [Tashkent Metro, hasStation, Paxtakor station]
-
A.
Kaladar Station
Kaladar Station is a small rural community within the township of Addington Highlands in eastern Ontario, Canada.
-
B.
Kargar station
Kargar station is a metro stop on Tehran’s urban rail network serving passengers along Line 6.
-
C.
Hamar Station
Hamar Station is a railway station in the town of Hamar in Innlandet county, Norway, serving as a regional transport hub on the country’s rail network.
-
D.
Poroy station
Poroy station is a railway station near Cusco, Peru, serving as a key departure point for trains traveling to Machu Picchu and the Sacred Valley.
-
E.
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.
- 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: Paxtakor station Triple: [Tashkent Metro, hasStation, Paxtakor station]
Generated description
Paxtakor station is a metro station in the Tashkent Metro system in Tashkent, Uzbekistan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Paxtakor station Target entity description: Paxtakor station is a metro station in the Tashkent Metro system in Tashkent, Uzbekistan.
-
A.
Kaladar Station
Kaladar Station is a small rural community within the township of Addington Highlands in eastern Ontario, Canada.
-
B.
Kargar station
Kargar station is a metro stop on Tehran’s urban rail network serving passengers along Line 6.
-
C.
Hamar Station
Hamar Station is a railway station in the town of Hamar in Innlandet county, Norway, serving as a regional transport hub on the country’s rail network.
-
D.
Poroy station
Poroy station is a railway station near Cusco, Peru, serving as a key departure point for trains traveling to Machu Picchu and the Sacred Valley.
-
E.
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.
- 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.