Triple
T5517133
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Milena Králíčková |
E144712
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Králíčková |
E437189
|
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: Králíčková | Statement: [Milena Králíčková, familyName, Králíčková]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Králíčková Context triple: [Milena Králíčková, familyName, Králíčková]
-
A.
Zelníčková
Zelníčková is a Czech surname, notably borne by Ivana Marie Zelníčková, the Czech-American businesswoman and former wife of Donald Trump.
-
B.
Ostružná
Ostružná is a river in the Czech Republic that serves as a tributary of the Úhlava River.
-
C.
Libuše
Libuše is a Czech opera by Bedřich Smetana, centered on the legendary princess Libuše who prophesies the glory of Prague and the Czech nation.
-
D.
Krompachy
Krompachy is a small industrial town in eastern Slovakia known historically for its ironworks and metalworking industry.
-
E.
Nosková
chosen
Nosková is a Czech surname, typically the feminine form of the surname Nosek.
- 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_69c008f77ff88190b0cd50ca207295d1 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01f5e8ce08190b7f5f2131bebcd4f |
completed | March 22, 2026, 4:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c027dd848481908052007e89c3f634 |
completed | March 22, 2026, 5:33 p.m. |
Created at: March 22, 2026, 3:33 p.m.