Triple
T17842724
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kupa River |
E445572
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Metlika |
—
|
NE NERFINISHED |
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: Metlika | Statement: [Kupa River, flowsThrough, Metlika]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Metlika Context triple: [Kupa River, flowsThrough, Metlika]
-
A.
Metlika
chosen
Metlika is a historic town in southeastern Slovenia known for its wine-making tradition and cultural heritage in the Bela Krajina region.
-
B.
Melika
Melika is a historic oasis town in Algeria’s M’zab Valley, known for its traditional Ibadi Muslim community and distinctive Saharan architecture.
-
C.
Milina
Milina is a seaside village in the Pelion region of central Greece, known for its tranquil beaches and views across the Pagasetic Gulf.
-
D.
Nadiža
Nadiža is a river in the western Balkans, known for its clear waters and scenic course through the mountainous border region between Slovenia and Italy.
-
E.
Mila
Mila is a leading artificial intelligence research institute based in Quebec, renowned for its work in deep learning and machine learning.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9f1a6d881909f024bc603111cdb |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e48ff8730881908cc8e1b572fa0af8 |
completed | April 19, 2026, 8:19 a.m. |
Created at: April 10, 2026, 10:16 a.m.