Triple
T12768318
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | W.E. (film) |
E305180
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object | Evgeni |
E246656
|
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: Evgeni | Statement: [W.E. (film), character, Evgeni]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Evgeni Context triple: [W.E. (film), character, Evgeni]
-
A.
Evgeni
chosen
Evgeni is a masculine given name most notably associated with Russian-born NHL star Evgeni Malkin.
-
B.
Nikita Gusev
Nikita Gusev is a Russian professional ice hockey forward known for his high-end playmaking skills and success in both the Kontinental Hockey League and the NHL.
-
C.
Zhenya Lukashin
Zhenya Lukashin is the hapless, mild-mannered Moscow doctor whose drunken New Year’s Eve misadventure drives the plot of the classic Soviet romantic comedy “The Irony of Fate.”
-
D.
Ilya
Ilya is a common Russian given name, notably borne by star ice hockey player Ilya Kovalchuk.
-
E.
Igor Babuschkin
Igor Babuschkin is an AI researcher and engineer known for his work on large language models at organizations such as DeepMind, OpenAI, and later xAI.
- 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_69d7bdf2b43c819098ae5aa68e61ea58 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96df3b2f88190b37b696400178795 |
completed | April 10, 2026, 9:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f684f750a08190abf6122baa579bc4 |
completed | May 2, 2026, 11:12 p.m. |
Created at: April 9, 2026, 5:28 p.m.