Triple
T14993367
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miro |
E373892
|
entity |
| Predicate | canBeDiminutiveOf |
P456
|
FINISHED |
| Object | Miroslav |
E95945
|
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: Miroslav | Statement: [Miro, canBeDiminutiveOf, Miroslav]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Miroslav Context triple: [Miro, canBeDiminutiveOf, Miroslav]
-
A.
Miroslav
chosen
Miroslav is a common Slavic male given name, notably borne by Slovak ice hockey star Miroslav Šatan.
-
B.
Ladislav
Ladislav is a masculine given name of Slavic origin, commonly used in Central and Eastern Europe.
-
C.
Timotej
Timotej is a masculine given name, common in Slavic countries, that is equivalent to Timothy.
-
D.
Jaroslav
Jaroslav is a masculine given name of Slavic origin, commonly used in Czech, Slovak, Polish, and other Slavic languages.
-
E.
Lubomir
Lubomir is a mountain peak in southern Poland’s Beskid Wyspowy range, known for its scenic views and hiking trails.
- 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_69d85ccc84388190aa151e5173370c8d |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded716ebb481908224d2d4f7561b03 |
completed | April 15, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fea5aca8488190bf00bdbd7c4fb535 |
completed | May 9, 2026, 3:10 a.m. |
Created at: April 10, 2026, 2:53 a.m.