Triple
T4526880
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Konya Province |
E106200
|
entity |
| Predicate | hasDistrict |
P459
|
FINISHED |
| Object |
Seydişehir
Seydişehir is a town and district in central Turkey known for its aluminum industry and location within Konya Province.
|
E503163
|
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: Seydişehir | Statement: [Konya Province, hasDistrict, Seydişehir]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Seydişehir Context triple: [Konya Province, hasDistrict, Seydişehir]
-
A.
Kütahya
Kütahya is a historic city in western Turkey known for its Ottoman-era architecture and traditional ceramic and tile production.
-
B.
Aksaray
Aksaray is a historic city in central Turkey known for its location on the ancient Silk Road and its proximity to the Cappadocia region.
-
C.
Nevşehir
Nevşehir is a city in central Turkey that serves as the main urban center and gateway to the historic, cave-dotted region of Cappadocia.
-
D.
Doğubayazıt
Doğubayazıt is a town in eastern Turkey near the Iranian border, known as a gateway to Mount Ararat and for its historic Ishak Pasha Palace.
-
E.
Eskişehir
Eskişehir is a major university and industrial city in northwestern Turkey, known for its vibrant student life, modern urban design, and rich cultural heritage.
- 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: Seydişehir Triple: [Konya Province, hasDistrict, Seydişehir]
Generated description
Seydişehir is a town and district in central Turkey known for its aluminum industry and location within Konya Province.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Seydişehir Target entity description: Seydişehir is a town and district in central Turkey known for its aluminum industry and location within Konya Province.
-
A.
Kütahya
Kütahya is a historic city in western Turkey known for its Ottoman-era architecture and traditional ceramic and tile production.
-
B.
Aksaray
Aksaray is a historic city in central Turkey known for its location on the ancient Silk Road and its proximity to the Cappadocia region.
-
C.
Nevşehir
Nevşehir is a city in central Turkey that serves as the main urban center and gateway to the historic, cave-dotted region of Cappadocia.
-
D.
Doğubayazıt
Doğubayazıt is a town in eastern Turkey near the Iranian border, known as a gateway to Mount Ararat and for its historic Ishak Pasha Palace.
-
E.
Eskişehir
Eskişehir is a major university and industrial city in northwestern Turkey, known for its vibrant student life, modern urban design, and rich cultural heritage.
- 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_69bd43f3d6e08190a91824f833d51bbe |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd57760f4481908f69ce82be63d7f8 |
completed | March 20, 2026, 2:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69beef7bd5048190b19be461683c864c |
completed | March 21, 2026, 7:20 p.m. |
| NEDg | Description generation | batch_69bef05ed6188190a86ec2a3bebd21dc |
completed | March 21, 2026, 7:24 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bef0e41b508190baca5e15efb65ee9 |
completed | March 21, 2026, 7:26 p.m. |
Created at: March 20, 2026, 1:03 p.m.