Triple
T9007626
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kızılırmak River |
E215383
|
entity |
| Predicate | passesNear |
P416
|
FINISHED |
| Object |
Bafra
Bafra is a town and district in Turkey’s Samsun Province, known for its fertile agricultural plain and proximity to the Black Sea coast.
|
E774242
|
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: Bafra | Statement: [Kızılırmak River, passesNear, Bafra]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bafra Context triple: [Kızılırmak River, passesNear, Bafra]
-
A.
Düzce
Düzce is a city in northwestern Turkey known for its location between Istanbul and Ankara and its proximity to the Black Sea.
-
B.
Körfez
Körfez is a coastal industrial city and district in Turkey’s Kocaeli Province, located along the Gulf of İzmit in the Marmara region.
-
C.
Kemalpaşa
Kemalpaşa is a district and town in western Turkey known for its cherry production and proximity to the city of İzmir.
-
D.
Meram
Meram is a central district and municipality of Konya in Turkey, known for its historic neighborhoods, gardens, and cultural heritage.
-
E.
Darıca
Darıca is a coastal town and district in northwestern Turkey, situated on the Sea of Marmara and known for its zoo, recreation areas, and proximity to Istanbul.
- 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: Bafra Triple: [Kızılırmak River, passesNear, Bafra]
Generated description
Bafra is a town and district in Turkey’s Samsun Province, known for its fertile agricultural plain and proximity to the Black Sea coast.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bafra Target entity description: Bafra is a town and district in Turkey’s Samsun Province, known for its fertile agricultural plain and proximity to the Black Sea coast.
-
A.
Düzce
Düzce is a city in northwestern Turkey known for its location between Istanbul and Ankara and its proximity to the Black Sea.
-
B.
Körfez
Körfez is a coastal industrial city and district in Turkey’s Kocaeli Province, located along the Gulf of İzmit in the Marmara region.
-
C.
Kemalpaşa
Kemalpaşa is a district and town in western Turkey known for its cherry production and proximity to the city of İzmir.
-
D.
Meram
Meram is a central district and municipality of Konya in Turkey, known for its historic neighborhoods, gardens, and cultural heritage.
-
E.
Darıca
Darıca is a coastal town and district in northwestern Turkey, situated on the Sea of Marmara and known for its zoo, recreation areas, and proximity to Istanbul.
- 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_69ca83a2bf088190986ee7a8eb90407d |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc69bdc5fc819081015f4adacf9fd4 |
completed | April 1, 2026, 12:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfeb671e488190920fb1780e4ad48d |
completed | April 3, 2026, 4:31 p.m. |
| NEDg | Description generation | batch_69cfedb37584819083038f1498b00886 |
completed | April 3, 2026, 4:41 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cfee063adc8190a1f2bd447f137e56 |
completed | April 3, 2026, 4:42 p.m. |
Created at: March 30, 2026, 7:05 p.m.