Triple
T735935
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mileva Marić |
E14931
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Mileva |
E2645
|
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: Mileva | Statement: [Mileva Marić, givenName, Mileva]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mileva Context triple: [Mileva Marić, givenName, Mileva]
-
A.
Mileva Marić
chosen
Mileva Marić was a Serbian mathematician and physicist who was Albert Einstein’s first wife and an early collaborator during his formative scientific years.
-
B.
Lydia Lopokova
Lydia Lopokova was a celebrated Russian ballerina of the early 20th century who became prominent in British cultural life through her performances and her marriage into the Bloomsbury circle.
-
C.
Ruzena Bajcsy
Ruzena Bajcsy is a pioneering computer scientist and engineer known for her influential work in robotics, computer vision, and artificial intelligence.
-
D.
Elizabeth Stamatina Fey
Elizabeth Stamatina Fey, better known as Tina Fey, is an American comedian, writer, actress, and producer renowned for her work on Saturday Night Live and creating the sitcom 30 Rock.
-
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_69a4934d9930819099eed80096b0597d |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a5da30b88190afbd12ae6109cc1b |
completed | March 1, 2026, 8:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a65e3df8e48190905f89acbf7f3667 |
completed | March 3, 2026, 4:06 a.m. |
Created at: March 1, 2026, 7:37 p.m.