Triple
T6058470
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Czarna Góra |
E134971
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Białka Tatrzańska |
E98231
|
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: Białka Tatrzańska | Statement: [Czarna Góra, locatedNear, Białka Tatrzańska]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Białka Tatrzańska Context triple: [Czarna Góra, locatedNear, Białka Tatrzańska]
-
A.
Białka Tatrzańska
chosen
Białka Tatrzańska is a popular mountain village and ski resort in southern Poland, known for its thermal baths and access to the Tatra Mountains.
-
B.
Sokółka
Sokółka is a small town in northeastern Poland known for its location in the Podlasie region near the border with Belarus.
-
C.
Słomniki
Słomniki is a small town in southern Poland, located in the Lesser Poland Voivodeship.
-
D.
Świątniki Górne
Świątniki Górne is a small town in southern Poland, situated near Kraków and known for its traditional locksmithing and metalworking industries.
-
E.
Ciecień
Ciecień is a mountain peak in southern Poland that forms part of the Beskid Wyspowy range in the Western Carpathians.
- 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_69c00877b6d4819096b0e163728b73a3 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c0570e64408190ae7a2504f63bb58a |
completed | March 22, 2026, 8:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c11d0e06288190b4389b43825d5929 |
completed | March 23, 2026, 10:59 a.m. |
Created at: March 22, 2026, 4:10 p.m.