Triple
T22697520
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Muszyna–Plaveč rail crossing |
E561222
|
entity |
| Predicate | hasLanguageAtBorder |
P77518
|
FINISHED |
| Object | Polish |
—
|
LITERAL 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: Polish | Statement: [Muszyna–Plaveč rail crossing, hasLanguageAtBorder, Polish]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLanguageAtBorder Context triple: [Muszyna–Plaveč rail crossing, hasLanguageAtBorder, Polish]
-
A.
languageAlongBorder
chosen
Indicates that a particular language is spoken or prevalent along the border between two regions or entities.
-
B.
hasLanguageOfSide
Indicates that an entity uses or is associated with a particular language on a specific side or aspect (e.g., one side of a bilingual object or interface).
-
C.
hasLanguageOnSides
Indicates that an object or medium features written or spoken language present on multiple sides or surfaces.
-
D.
hasLanguageOfSurroundingCountries
Indicates that an entity uses or includes the languages commonly spoken in the countries that geographically surround it.
-
E.
hasNeighboringLanguages
Indicates that two languages are geographically or regionally adjacent to each other in their areas of use.
- F. None of above.
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_69e2454e615481909c177440be559d2c |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1789f26848190bcc5a99e3ed909e7 |
completed | April 29, 2026, 3:18 a.m. |
| PD | Predicate disambiguation | batch_69ee62bd657c81909f7b01245b080a5f |
completed | April 26, 2026, 7:08 p.m. |
Created at: April 17, 2026, 3:14 p.m.