Triple
T13266244
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aydın Province |
E315931
|
entity |
| Predicate | containsDistrict |
P22582
|
FINISHED |
| Object |
Buharkent
Buharkent is a small district and town in western Turkey known for its geothermal resources and agricultural production within Aydın Province.
|
E1088161
|
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: Buharkent | Statement: [Aydın Province, containsDistrict, Buharkent]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Buharkent Context triple: [Aydın Province, containsDistrict, Buharkent]
-
A.
Orhangazi
Orhangazi is a town and district in northwestern Turkey known for its olive cultivation and location near Lake İznik in Bursa Province.
-
B.
Gürbulak
Gürbulak is a Turkish border village and crossing point on the frontier with Iran, serving as a key gateway between the two countries.
-
C.
Batu City
Batu City is a highland tourist city in East Java, Indonesia, known for its cool climate, mountain scenery, and numerous recreational attractions.
-
D.
Büyükerşen
Büyükerşen is a Turkish surname most prominently associated with Yılmaz Büyükerşen, a well-known academic and long-serving mayor of Eskişehir.
-
E.
Keban
Keban is a town in eastern Turkey located near the Euphrates River, known primarily for its proximity to the large hydroelectric Keban Dam.
- 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: Buharkent Triple: [Aydın Province, containsDistrict, Buharkent]
Generated description
Buharkent is a small district and town in western Turkey known for its geothermal resources and agricultural production within Aydın Province.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Buharkent Target entity description: Buharkent is a small district and town in western Turkey known for its geothermal resources and agricultural production within Aydın Province.
-
A.
Orhangazi
Orhangazi is a town and district in northwestern Turkey known for its olive cultivation and location near Lake İznik in Bursa Province.
-
B.
Gürbulak
Gürbulak is a Turkish border village and crossing point on the frontier with Iran, serving as a key gateway between the two countries.
-
C.
Batu City
Batu City is a highland tourist city in East Java, Indonesia, known for its cool climate, mountain scenery, and numerous recreational attractions.
-
D.
Büyükerşen
Büyükerşen is a Turkish surname most prominently associated with Yılmaz Büyükerşen, a well-known academic and long-serving mayor of Eskişehir.
-
E.
Keban
Keban is a town in eastern Turkey located near the Euphrates River, known primarily for its proximity to the large hydroelectric Keban Dam.
- 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_69d806b1d9ac8190852c5571d5bd5f0f |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d9901e44bc8190966f87ae219d6bf4 |
completed | April 11, 2026, 12:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd27f0e59c8190b40213e999c75feb |
completed | May 8, 2026, 12:01 a.m. |
| NEDg | Description generation | batch_69fd2b2363f881909e04edd850166dd5 |
completed | May 8, 2026, 12:15 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd2cf1a1248190a97644dadf1717bc |
completed | May 8, 2026, 12:23 a.m. |
Created at: April 9, 2026, 9:25 p.m.