Triple
T18902441
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uprooted |
E462368
|
entity |
| Predicate | hasMainCharacter |
P1183
|
FINISHED |
| Object | Agnieszka |
—
|
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: Agnieszka | Statement: [Uprooted, hasMainCharacter, Agnieszka]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Agnieszka Context triple: [Uprooted, hasMainCharacter, Agnieszka]
-
A.
Agnieszka
chosen
Agnieszka is a Polish feminine given name, commonly regarded as the Polish form of Agnes.
-
B.
Katarzyna
Katarzyna is a common Polish female given name, equivalent to Catherine in English.
-
C.
Zuzanna
Zuzanna is a feminine given name, primarily used in Slavic countries, that is a variant of the name Susanna.
-
D.
Dorota
Dorota is a feminine given name used in various Slavic and European cultures, often considered a variant of Dorothy.
-
E.
Zofia
Zofia is a feminine given name of Slavic origin, particularly common in Poland and other Central and Eastern European countries.
- 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_69d8dcfd05bc819088903cca13cc2846 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c52a4b4c8190b5821996e3c1741d |
completed | April 20, 2026, 6:18 a.m. |
Created at: April 10, 2026, 11:58 a.m.