Triple
T1686428
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mikhail |
E36451
|
entity |
| Predicate | hasDiminutive |
P456
|
FINISHED |
| Object | Misha |
E36451
|
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: Misha | Statement: [Mikhail, hasDiminutive, Misha]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Misha Context triple: [Mikhail, hasDiminutive, Misha]
-
A.
Mila
Mila is a leading artificial intelligence research institute based in Quebec, renowned for its work in deep learning and machine learning.
-
B.
Sasha
Sasha is a common Russian diminutive form of the given name Alexander (and also Alexandra).
-
C.
Mikhail
chosen
Mikhail is a common Russian male given name, famously borne by Soviet leader Mikhail Gorbachev.
-
D.
Yuri
Yuri is a common Russian given name, famously borne by Yuri Gagarin, the first human to journey into outer space.
-
E.
Aloysya
Aloysya is a given name, typically a feminine variant of Aloysius, used in various cultures and languages.
- 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_69a886151508819084fa7f1ce6e05577 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa6293c368819094ab0f615e418647 |
completed | March 6, 2026, 5:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad71c344fc8190926db828cf09550e |
completed | March 8, 2026, 12:55 p.m. |
Created at: March 4, 2026, 7:29 p.m.