Triple
T15046819
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zima |
E379249
|
entity |
| Predicate | languageOfOrigin |
P151
|
FINISHED |
| Object | Czech language |
E73024
|
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: Czech language | Statement: [Zima, languageOfOrigin, Czech language]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Czech language Context triple: [Zima, languageOfOrigin, Czech language]
-
A.
Czech language
chosen
Czech language is a West Slavic language spoken primarily in the Czech Republic and known for its rich literary tradition and complex grammar.
-
B.
Czech
Czech refers to a West Slavic ethnic group native to the Czech Republic, historically associated with the region of Bohemia and the Czech language.
-
C.
Czech–Slovak languages
The Czech–Slovak languages are a closely related group of Slavic languages, primarily including Czech and Slovak, spoken in Central Europe.
-
D.
Slovak language
The Slovak language is a West Slavic language spoken primarily in Slovakia and closely related to Czech and Polish.
-
E.
Letiny
Letiny is a small municipality and village located in the Plzeň Region of the Czech Republic.
- 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_69d85cd64d108190853797a95c11cc45 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69deda8e64e48190873104a02a676ff3 |
completed | April 15, 2026, 12:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe9de73614819098b7a88624407d0e |
completed | May 9, 2026, 2:37 a.m. |
Created at: April 10, 2026, 3 a.m.