Triple
T14653167
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mariska |
E344041
|
entity |
| Predicate | diminutiveOf |
P456
|
FINISHED |
| Object | Mária |
E370388
|
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: Mária | Statement: [Mariska, diminutiveOf, Mária]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mária Context triple: [Mariska, diminutiveOf, Mária]
-
A.
Mária
chosen
Mária is the Hungarian and Slovak form of the given name Mary, commonly used in Central and Eastern Europe.
-
B.
Terézia Mora
Terézia Mora is a Hungarian-born German writer and translator acclaimed for her innovative prose and contributions to contemporary German-language literature.
-
C.
Antónia
Antónia is a feminine given name commonly used in various European languages, often as a variant of Antonia.
-
D.
Terézia
Terézia is the given name of the Hungarian-born German writer and translator Terézia Mora, known for her award-winning novels and screenplays.
-
E.
Vilma Bánky
Vilma Bánky was a Hungarian-born silent film actress best known as a leading lady in 1920s Hollywood, particularly in romantic dramas opposite stars like Rudolph Valentino.
- 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_69d822e1a2cc81908e5bb93cf61ce3cc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb518f7dc8190877997ea4cd3eed2 |
completed | April 14, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fde17283608190a8351b366cac5e4f |
completed | May 8, 2026, 1:13 p.m. |
Created at: April 10, 2026, 1:27 a.m.