Triple
T13251149
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | M1 line |
E315531
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Akköprü station
Akköprü station is a metro stop on Ankara’s M1 line, serving passengers in the Akköprü area of Turkey’s capital.
|
E1031012
|
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: Akköprü station | Statement: [M1 line, hasStation, Akköprü station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Akköprü station Context triple: [M1 line, hasStation, Akköprü 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.
Kazlıçeşme station
Kazlıçeşme station is a major railway and commuter rail stop in Istanbul that serves as one of the key terminals on the Marmaray cross-Bosphorus rail system.
-
C.
Fahrettin Altay station
Fahrettin Altay station is a major western terminus and transfer hub on the İzmir Metro system in İzmir, Turkey.
-
D.
Alsancak railway station
Alsancak railway station is a historic and central railway terminus in İzmir, Turkey, serving as one of the city's main transportation hubs.
-
E.
Kargar station
Kargar station is a metro stop on Tehran’s urban rail network serving passengers along Line 6.
- 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: Akköprü station Triple: [M1 line, hasStation, Akköprü station]
Generated description
Akköprü station is a metro stop on Ankara’s M1 line, serving passengers in the Akköprü area of Turkey’s capital.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Akköprü station Target entity description: Akköprü station is a metro stop on Ankara’s M1 line, serving passengers in the Akköprü area of Turkey’s capital.
-
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.
Kazlıçeşme station
Kazlıçeşme station is a major railway and commuter rail stop in Istanbul that serves as one of the key terminals on the Marmaray cross-Bosphorus rail system.
-
C.
Fahrettin Altay station
Fahrettin Altay station is a major western terminus and transfer hub on the İzmir Metro system in İzmir, Turkey.
-
D.
Alsancak railway station
Alsancak railway station is a historic and central railway terminus in İzmir, Turkey, serving as one of the city's main transportation hubs.
-
E.
Kargar station
Kargar station is a metro stop on Tehran’s urban rail network serving passengers along Line 6.
- 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_69d806b1072881909e46bd212259c5f0 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98f73423c8190932a9edac56df383 |
completed | April 11, 2026, 12:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f70a3b8ca48190863aff25f12d0e7e |
completed | May 3, 2026, 8:41 a.m. |
| NEDg | Description generation | batch_69f70b37ebe081909ed2ae0f42ccad2d |
completed | May 3, 2026, 8:45 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f70ca343f08190b6484f464ed40810 |
completed | May 3, 2026, 8:51 a.m. |
Created at: April 9, 2026, 9:24 p.m.