Triple
T1642510
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hillsboro |
E35503
|
entity |
| Predicate | hasSisterCity |
P919
|
FINISHED |
| Object |
Giresun, Turkey
Giresun, Turkey is a Black Sea coastal city in northeastern Turkey known for its hazelnut production and lush, hilly landscape.
|
E186597
|
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: Giresun, Turkey | Statement: [Hillsboro, hasSisterCity, Giresun, Turkey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Giresun, Turkey Context triple: [Hillsboro, hasSisterCity, Giresun, Turkey]
-
A.
Trabzon
Trabzon is a historic city in northeastern Turkey that serves as a major Black Sea port and regional cultural and commercial center.
-
B.
Samsun
Samsun is a major Turkish port city on the Black Sea coast, known as an important regional hub for maritime trade and industry.
-
C.
Yalova
Yalova is a small coastal city in northwestern Turkey, known for its thermal springs, seaside promenade, and proximity to Istanbul across the Sea of Marmara.
-
D.
Ardahan
Ardahan is a town in northeastern Turkey that serves as the capital of Ardahan Province near the border with Georgia.
-
E.
Erzurum, Turkey
Erzurum, Turkey is a historic city in eastern Anatolia known for its Ottoman-era architecture, harsh winters, and role as a regional cultural and educational center.
- 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: Giresun, Turkey Triple: [Hillsboro, hasSisterCity, Giresun, Turkey]
Generated description
Giresun, Turkey is a Black Sea coastal city in northeastern Turkey known for its hazelnut production and lush, hilly landscape.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Giresun, Turkey Target entity description: Giresun, Turkey is a Black Sea coastal city in northeastern Turkey known for its hazelnut production and lush, hilly landscape.
-
A.
Trabzon
Trabzon is a historic city in northeastern Turkey that serves as a major Black Sea port and regional cultural and commercial center.
-
B.
Samsun
Samsun is a major Turkish port city on the Black Sea coast, known as an important regional hub for maritime trade and industry.
-
C.
Yalova
Yalova is a small coastal city in northwestern Turkey, known for its thermal springs, seaside promenade, and proximity to Istanbul across the Sea of Marmara.
-
D.
Ardahan
Ardahan is a town in northeastern Turkey that serves as the capital of Ardahan Province near the border with Georgia.
-
E.
Erzurum, Turkey
Erzurum, Turkey is a historic city in eastern Anatolia known for its Ottoman-era architecture, harsh winters, and role as a regional cultural and educational center.
- 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_69a88604618c81908b41f6429c431eb6 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a90a3f4d8c8190aa0a44d1c9b1a7f0 |
completed | March 5, 2026, 4:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad60a0096c81909dc723d0db95481e |
completed | March 8, 2026, 11:42 a.m. |
| NEDg | Description generation | batch_69ad620fe35481909bf4751001e29161 |
completed | March 8, 2026, 11:48 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad626d42388190b6a961a84333bd21 |
completed | March 8, 2026, 11:50 a.m. |
Created at: March 4, 2026, 7:28 p.m.