Triple
T2837431
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | İzmir Metro |
E62383
|
entity |
| Predicate | hasKeyStation |
P22144
|
FINISHED |
| Object |
Bornova station
Bornova station is a key terminal stop on the İzmir Metro system serving the Bornova district of İzmir, Turkey.
|
E306132
|
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: Bornova station | Statement: [İzmir Metro, hasKeyStation, Bornova station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bornova station Context triple: [İzmir Metro, hasKeyStation, Bornova 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.
Borna station
Borna station is a regional railway station in the town of Borna in Saxony, Germany, serving passenger traffic on the Chemnitz–Leipzig rail corridor.
-
C.
Kommunarka station
Kommunarka station is a southern terminal metro station on Moscow’s Sokolnicheskaya Line, serving the rapidly developing Kommunarka district.
-
D.
Khimvolokno station
Khimvolokno station is a stop on the Volgograd Metrotram light rail system in Volgograd, Russia.
-
E.
Lichtenberg station
Lichtenberg station is a major railway and transport hub in the Lichtenberg district of Berlin, Germany, serving regional, long-distance, and urban transit lines.
- 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: Bornova station Triple: [İzmir Metro, hasKeyStation, Bornova station]
Generated description
Bornova station is a key terminal stop on the İzmir Metro system serving the Bornova district of İzmir, Turkey.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bornova station Target entity description: Bornova station is a key terminal stop on the İzmir Metro system serving the Bornova district of İzmir, Turkey.
-
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.
Borna station
Borna station is a regional railway station in the town of Borna in Saxony, Germany, serving passenger traffic on the Chemnitz–Leipzig rail corridor.
-
C.
Kommunarka station
Kommunarka station is a southern terminal metro station on Moscow’s Sokolnicheskaya Line, serving the rapidly developing Kommunarka district.
-
D.
Khimvolokno station
Khimvolokno station is a stop on the Volgograd Metrotram light rail system in Volgograd, Russia.
-
E.
Lichtenberg station
Lichtenberg station is a major railway and transport hub in the Lichtenberg district of Berlin, Germany, serving regional, long-distance, and urban transit lines.
- 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_69ab4c3c39188190955b9c49d98463d8 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abe08ae5048190a0a3b573d9a5fdbc |
completed | March 7, 2026, 8:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b01d7705108190a986d3def0c08f7b |
completed | March 10, 2026, 1:32 p.m. |
| NEDg | Description generation | batch_69b01fbe078c8190ba786122afb4f54b |
completed | March 10, 2026, 1:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b020f0f6208190b134f3472b50fb1b |
completed | March 10, 2026, 1:47 p.m. |
Created at: March 6, 2026, 10:01 p.m.