Triple
T15777642
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baku Metro |
E382528
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Koroglu station
Koroglu station is a metro station in Baku, Azerbaijan, serving as part of the city's Baku Metro rapid transit system.
|
E1183955
|
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: Koroglu station | Statement: [Baku Metro, hasStation, Koroglu station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Koroglu station Context triple: [Baku Metro, hasStation, Koroglu station]
-
A.
Konak station
Konak station is a central underground stop on the İzmir Metro system, serving as one of the main transit hubs in the heart of İzmir, Turkey.
-
B.
Kargar station
Kargar station is a metro stop on Tehran’s urban rail network serving passengers along Line 6.
-
C.
Beruniy station
Beruniy station is a metro station in Tashkent, Uzbekistan, serving as part of the city's Tashkent Metro rapid transit system.
-
D.
Icherisheher station
Icherisheher station is a metro station on the Baku Metro system serving the historic Old City area of Baku, Azerbaijan.
-
E.
Kızılay station
Kızılay station is a major underground metro hub in central Ankara, Turkey, serving as a key interchange point for multiple lines in the city’s rapid transit 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: Koroglu station Triple: [Baku Metro, hasStation, Koroglu station]
Generated description
Koroglu station is a metro station in Baku, Azerbaijan, serving as part of the city's Baku Metro rapid transit system.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Koroglu station Target entity description: Koroglu station is a metro station in Baku, Azerbaijan, serving as part of the city's Baku Metro rapid transit system.
-
A.
Konak station
Konak station is a central underground stop on the İzmir Metro system, serving as one of the main transit hubs in the heart of İzmir, Turkey.
-
B.
Kargar station
Kargar station is a metro stop on Tehran’s urban rail network serving passengers along Line 6.
-
C.
Beruniy station
Beruniy station is a metro station in Tashkent, Uzbekistan, serving as part of the city's Tashkent Metro rapid transit system.
-
D.
Icherisheher station
Icherisheher station is a metro station on the Baku Metro system serving the historic Old City area of Baku, Azerbaijan.
-
E.
Kızılay station
Kızılay station is a major underground metro hub in central Ankara, Turkey, serving as a key interchange point for multiple lines in the city’s rapid transit 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_69ffb59779788190a393237f5293fe8d |
completed | May 9, 2026, 10:30 p.m. |
| NEDg | Description generation | batch_69ffb62f3d8881908ede4a9a4b53bef2 |
completed | May 9, 2026, 10:33 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffb6f3154481909632913f4d7cfdba |
completed | May 9, 2026, 10:36 p.m. |
Created at: April 10, 2026, 4:47 a.m.