Triple
T14127852
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kırıkkale Province |
E340080
|
entity |
| Predicate | hasDistrict |
P459
|
FINISHED |
| Object |
Bahşili
Bahşili is a small town and district in central Turkey known for its rural character within Kırıkkale Province.
|
E1091075
|
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: Bahşili | Statement: [Kırıkkale Province, hasDistrict, Bahşili]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bahşili Context triple: [Kırıkkale Province, hasDistrict, Bahşili]
-
A.
Buharkent
Buharkent is a small district and town in western Turkey known for its geothermal resources and agricultural production within Aydın 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.
Ballıhisar
Ballıhisar is a village in modern-day Turkey located near the archaeological site of the ancient Phrygian city of Pessinus.
-
D.
Büyükerşen
Büyükerşen is a Turkish surname most prominently associated with Yılmaz Büyükerşen, a well-known academic and long-serving mayor of Eskişehir.
-
E.
Şahinbey
Şahinbey is a central district and municipality of Gaziantep in southeastern Turkey, known as a major urban and commercial area of the city.
- 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: Bahşili Triple: [Kırıkkale Province, hasDistrict, Bahşili]
Generated description
Bahşili is a small town and district in central Turkey known for its rural character within Kırıkkale Province.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bahşili Target entity description: Bahşili is a small town and district in central Turkey known for its rural character within Kırıkkale Province.
-
A.
Buharkent
Buharkent is a small district and town in western Turkey known for its geothermal resources and agricultural production within Aydın 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.
Ballıhisar
Ballıhisar is a village in modern-day Turkey located near the archaeological site of the ancient Phrygian city of Pessinus.
-
D.
Büyükerşen
Büyükerşen is a Turkish surname most prominently associated with Yılmaz Büyükerşen, a well-known academic and long-serving mayor of Eskişehir.
-
E.
Şahinbey
Şahinbey is a central district and municipality of Gaziantep in southeastern Turkey, known as a major urban and commercial area of the city.
- 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_69d81c6a95b481909e39111e0c1f31ee |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de6098013c8190b1bac9d3fff60acd |
completed | April 14, 2026, 3:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd3d043860819099526cbae1b1ef18 |
completed | May 8, 2026, 1:31 a.m. |
| NEDg | Description generation | batch_69fd3e13914c81908f4dcda7f0f6a927 |
completed | May 8, 2026, 1:36 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd3ee3f66081909301276aeee05350 |
completed | May 8, 2026, 1:39 a.m. |
Created at: April 9, 2026, 10:22 p.m.