Triple
T13352744
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ankara commuter rail (Başkentray) |
E318108
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Kayaş station
Kayaş station is a railway station in Ankara, Turkey, serving as one of the stops on the city's Başkentray commuter rail system.
|
E1035813
|
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: Kayaş station | Statement: [Ankara commuter rail (Başkentray), hasStation, Kayaş station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kayaş station Context triple: [Ankara commuter rail (Başkentray), hasStation, Kayaş station]
-
A.
Kargar station
Kargar station is a metro stop on Tehran’s urban rail network serving passengers along Line 6.
-
B.
Asan Station
Asan Station is a railway station in Asan, South Korea, serving as a regional transit hub connecting local and intercity rail services.
-
C.
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.
-
D.
Akköprü station
Akköprü station is a metro stop on Ankara’s M1 line, serving passengers in the Akköprü area of Turkey’s capital.
-
E.
Kamayut Railway Station
Kamayut Railway Station is a local train station serving the Kamayut Township area of Yangon, Myanmar, as part of the city's commuter rail 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: Kayaş station Triple: [Ankara commuter rail (Başkentray), hasStation, Kayaş station]
Generated description
Kayaş station is a railway station in Ankara, Turkey, serving as one of the stops on the city's Başkentray commuter rail system.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kayaş station Target entity description: Kayaş station is a railway station in Ankara, Turkey, serving as one of the stops on the city's Başkentray commuter rail system.
-
A.
Kargar station
Kargar station is a metro stop on Tehran’s urban rail network serving passengers along Line 6.
-
B.
Asan Station
Asan Station is a railway station in Asan, South Korea, serving as a regional transit hub connecting local and intercity rail services.
-
C.
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.
-
D.
Akköprü station
Akköprü station is a metro stop on Ankara’s M1 line, serving passengers in the Akköprü area of Turkey’s capital.
-
E.
Kamayut Railway Station
Kamayut Railway Station is a local train station serving the Kamayut Township area of Yangon, Myanmar, as part of the city's commuter rail 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_69d806b5a3c08190b42c267fb092f98a |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99e8d520881908aa23c7102b72b72 |
completed | April 11, 2026, 1:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f71f49e5548190b14d09daea628e6b |
completed | May 3, 2026, 10:11 a.m. |
| NEDg | Description generation | batch_69f721b1a5d88190b9075437c7ab81a5 |
completed | May 3, 2026, 10:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f72262ede4819095b3dc4c7cd63450 |
completed | May 3, 2026, 10:24 a.m. |
Created at: April 9, 2026, 9:32 p.m.