Triple
T13433068
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sakarya River |
E320159
|
entity |
| Predicate | hasMouthNear |
P350
|
FINISHED |
| Object | Karasu |
E823353
|
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: Karasu | Statement: [Sakarya River, hasMouthNear, Karasu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Karasu Context triple: [Sakarya River, hasMouthNear, Karasu]
-
A.
Karasu
chosen
Karasu is a coastal town and district in northwestern Turkey known for its Black Sea beaches and location within Sakarya Province.
-
B.
Karasu
Karasu is the former name of Medgidia, a city in southeastern Romania’s Dobruja region.
-
C.
Karasuwa
Karasuwa is a local government area in northeastern Nigeria known for its predominantly rural communities and agricultural activities within Yobe State.
-
D.
Kambe
Kambe is one of the Mijikenda sub-groups of the coastal Bantu peoples of Kenya, known for their distinct language and cultural traditions.
-
E.
Kawazu
Kawazu is a small coastal town in Shizuoka Prefecture, Japan, known for its early-blooming Kawazu-zakura cherry blossoms and hot spring resorts.
- 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_69d80761e6cc8190a90c844589998ecc |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaed41a5481908800033303224adb |
completed | April 12, 2026, 2:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7398da07081908c3eca6fc4213930 |
completed | May 3, 2026, 12:03 p.m. |
Created at: April 9, 2026, 9:40 p.m.