Triple
T5360469
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maria |
E103006
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Mariya |
E370383
|
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: Mariya | Statement: [Maria, hasVariant, Mariya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mariya Context triple: [Maria, hasVariant, Mariya]
-
A.
Marya
chosen
Marya is a feminine given name, often considered a variant of Mary and used in various cultures and languages.
-
B.
Aloysya
Aloysya is a given name, typically a feminine variant of Aloysius, used in various cultures and languages.
-
C.
Mila
Mila is a leading artificial intelligence research institute based in Quebec, renowned for its work in deep learning and machine learning.
-
D.
Nina
Nina is a feminine given name used in various cultures, often as a short form of names like Antonina or Giannina, and borne by numerous notable figures in the arts and public life.
-
E.
Nina
Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
- 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_69bd43daa3e4819090b59d127db70e57 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd86588af081908c846fcde65724da |
completed | March 20, 2026, 5:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf21ec83b88190abc227d93b4c1c49 |
completed | March 21, 2026, 10:55 p.m. |
Created at: March 20, 2026, 2:02 p.m.