Triple
T17253321
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hyundai i10 |
E418812
|
entity |
| Predicate | assembly |
P19323
|
FINISHED |
| Object | İzmit, Turkey |
E117800
|
NE FINISHED |
How this triple was built (2 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: İzmit, Turkey | Statement: [Hyundai i10, assembly, İzmit, Turkey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: İzmit, Turkey Context triple: [Hyundai i10, assembly, İzmit, Turkey]
-
A.
İzmit
chosen
İzmit is a city in northwestern Turkey on the Gulf of İzmit, historically significant as the site of ancient Nicomedia and an important industrial and transportation hub near Istanbul.
-
B.
Trabzon
Trabzon is a historic city in northeastern Turkey that serves as a major Black Sea port and regional cultural and commercial center.
-
C.
Samsun
Samsun is a major Turkish port city on the Black Sea coast, known as an important regional hub for maritime trade and industry.
-
D.
Giresun, Turkey
Giresun, Turkey is a Black Sea coastal city in northeastern Turkey known for its hazelnut production and lush, hilly landscape.
-
E.
Sakarya, Turkey
Sakarya, Turkey is an industrial and agricultural province in northwestern Turkey, known for its automotive manufacturing plants and strategic location near Istanbul.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d886d9ab108190b70edd8d17aa1204 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42e6a1b648190a8bb2deb67bbdfdc |
completed | April 19, 2026, 1:22 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0170fb89248190ae431ce51dfeaffd |
completed | May 11, 2026, 6:02 a.m. |
Created at: April 10, 2026, 5:39 a.m.