Triple
T3863129
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Karel Gut |
E91786
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Karel |
E71855
|
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: Karel | Statement: [Karel Gut, givenName, Karel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Karel Context triple: [Karel Gut, givenName, Karel]
-
A.
Karel
chosen
Karel is a given name, commonly used in Central and Eastern Europe, that corresponds to the English name Charles.
-
B.
Karel Roden
Karel Roden is a Czech actor known internationally for his roles in films such as "Hellboy," "The Bourne Supremacy," and various European and Hollywood productions.
-
C.
Havlíček
Havlíček is a Czech surname most famously associated with basketball Hall of Famer John Havlicek and several notable Czech cultural and public figures.
-
D.
Kája
Kája is a Czech diminutive form of the given name Karel.
-
E.
Karl
Karl is the given first name of Charles Proteus Steinmetz, the renowned German-American mathematician and electrical engineer who revolutionized the understanding of alternating current systems.
- 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_69aed9645f348190a9868e7cef56ab7e |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeec2417648190ad010189d304d119 |
completed | March 9, 2026, 3:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5123ad9188190a158721a6192cdae |
completed | March 14, 2026, 7:46 a.m. |
Created at: March 9, 2026, 3:19 p.m.