Triple
T15537548
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Masha |
E370387
|
entity |
| Predicate | relatedName |
P3889
|
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: [Masha, relatedName, Mariya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mariya Context triple: [Masha, relatedName, Mariya]
-
A.
Mirra
Mirra is an iRobot-designed robotic pool cleaner that autonomously scrubs and vacuums swimming pools.
-
B.
Marya
chosen
Marya is a feminine given name, often considered a variant of Mary and used in various cultures and languages.
-
C.
Nadya
Nadya is a feminine given name, often used as a diminutive of Nadezhda in Slavic cultures.
-
D.
Aloysya
Aloysya is a given name, typically a feminine variant of Aloysius, used in various cultures and languages.
-
E.
Mila
Mila is a leading artificial intelligence research institute based in Quebec, renowned for its work in deep learning and machine learning.
- 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_69d85cc521a08190921fb50319dddc34 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e0442f3c688190a599165e526af2ed |
completed | April 16, 2026, 2:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff3d626e688190bd93481cfd6cb255 |
completed | May 9, 2026, 1:57 p.m. |
Created at: April 10, 2026, 4:06 a.m.