Triple
T22503210
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Danuta Stenka |
E556326
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Danuta |
—
|
NE NERFINISHED |
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: Danuta | Statement: [Danuta Stenka, givenName, Danuta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Danuta Context triple: [Danuta Stenka, givenName, Danuta]
-
A.
Danuta
chosen
Danuta is a feminine given name of Slavic origin, particularly common in Poland and other Central and Eastern European countries.
-
B.
Dagmara
Dagmara is a feminine given name, primarily used in Slavic countries, that is a variant of the name Dagmar.
-
C.
Dorota
Dorota is a feminine given name used in various Slavic and European cultures, often considered a variant of Dorothy.
-
D.
Danuta Stenka
Danuta Stenka is a renowned Polish film, television, and theatre actress known for her powerful dramatic roles and extensive work in Polish cinema.
-
E.
Agata
Agata is a feminine given name commonly used in several European countries, derived from the Greek name Agatha meaning "good" or "kind."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e555edc81909ca803587dafd747 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15d5a01888190ba65a05616b63cbe |
completed | April 29, 2026, 1:22 a.m. |
Created at: April 16, 2026, 8:50 p.m.