Triple
T6726677
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Antonín Novotný |
E153534
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Antonín |
E129866
|
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: Antonín | Statement: [Antonín Novotný, givenName, Antonín]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Antonín Context triple: [Antonín Novotný, givenName, Antonín]
-
A.
Vojtech
Vojtech is a masculine given name of Slavic origin, commonly used in Central and Eastern Europe.
-
B.
Oldřich
Oldřich is a Czech masculine given name traditionally borne by several notable historical and cultural figures in the Czech lands.
-
C.
Antonin
chosen
Antonin is a masculine given name most notably borne by Antonin Scalia, a former Associate Justice of the United States Supreme Court.
-
D.
Antonín Zápotocký
Antonín Zápotocký was a Czechoslovak communist politician who served as both prime minister and later president during the early Cold War era.
-
E.
Karel Gut
Karel Gut was a prominent Czech ice hockey coach and former player who significantly influenced Czechoslovak and later Czech ice hockey at the international level.
- 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_69c6880afb988190ad88011b48ecfcba |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d152a4908190a041f2049240e7ca |
completed | March 27, 2026, 6:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c700a5428c81908d4484c3e3734076 |
completed | March 27, 2026, 10:11 p.m. |
Created at: March 27, 2026, 2:08 p.m.