Triple
T18559430
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pronya River |
E453592
|
entity |
| Predicate | hasNameInLanguage |
P15
|
FINISHED |
| Object | Проня |
E802535
|
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: Проня | Statement: [Pronya River, hasNameInLanguage, Проня]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Проня Context triple: [Pronya River, hasNameInLanguage, Проня]
-
A.
Pravonín
Pravonín is a small municipality and village in the Central Bohemian Region of the Czech Republic.
-
B.
Prabuty
Prabuty is a historic town in northern Poland known for its medieval heritage and location in the lake-dotted landscape of the former Warmia-Masuria region.
-
C.
Prosotsani
Prosotsani is a town and municipality in northern Greece, situated in the Drama regional unit of Eastern Macedonia and Thrace.
-
D.
Pronsk
chosen
Pronsk is a historic town in Ryazan Oblast, Russia, known for its medieval origins and role as a local administrative and cultural center.
-
E.
Prochnow
Prochnow is a German surname most notably associated with actor Jürgen Prochnow, known for his role in the film "Das Boot."
- 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_69d8d388b0c881908e610a1c45b52640 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e53808c3fc8190aac38b29296cee13 |
completed | April 19, 2026, 8:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a049ae61fac81909c17b6e9318ed36e |
completed | May 13, 2026, 3:38 p.m. |
Created at: April 10, 2026, 11:42 a.m.