Triple
T9261873
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Krzysztof Arciszewski |
E222597
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Krzysztof |
E296797
|
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: Krzysztof | Statement: [Krzysztof Arciszewski, givenName, Krzysztof]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Krzysztof Context triple: [Krzysztof Arciszewski, givenName, Krzysztof]
-
A.
Krzysztof
chosen
Krzysztof is a Polish given name, equivalent to Christopher, commonly used in Poland and among Polish-speaking communities.
-
B.
Grzegorz
Grzegorz is the Polish form of the given name Gregory, commonly used in Poland and among Polish-speaking communities.
-
C.
Paweł
Paweł is a common Polish given name, equivalent to the English name Paul.
-
D.
Wojciech
Wojciech is a masculine given name of Slavic origin, particularly common in Poland.
-
E.
Andrzej
Andrzej is the Polish given name equivalent to Andrew, commonly used for men in Poland and among Polish communities.
- 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_69ca841f2e808190a64f4c31903a1332 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd07189bc48190987315bd5d3ce4f1 |
completed | April 1, 2026, 11:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0e361a00881909b431dba0db978fd |
completed | April 4, 2026, 10:09 a.m. |
Created at: March 30, 2026, 7:32 p.m.