Triple
T7436050
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yuri Lopatin |
E171616
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Lopatin |
E171616
|
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: Lopatin | Statement: [Yuri Lopatin, familyName, Lopatin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lopatin Context triple: [Yuri Lopatin, familyName, Lopatin]
-
A.
Lopatin
chosen
Lopatin is a Russian surname borne by various notable individuals in fields such as the military, arts, and academia.
-
B.
Tupikov
Tupikov is a Russian surname most notably associated with Vasiliy Tupikov, a Soviet military figure.
-
C.
Lopatina
Lopatina is the feminine form of the Russian surname Lopatin.
-
D.
Tupolski
Tupolski is a hard-edged, morally ambiguous police detective in Martin McDonagh’s dark play "The Pillowman," known for his interrogations and psychological manipulation.
-
E.
Chebutykin
Chebutykin is the aging, disillusioned army doctor whose cynicism and emotional detachment embody the themes of lost hope and stagnation in Anton Chekhov’s play "Three Sisters."
- 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_69c68a64228c8190affaec2a8127ce7b |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f347f25081908e6086d4073295f5 |
completed | March 27, 2026, 9:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c856a8312881908a86c30706283a9c |
completed | March 28, 2026, 10:31 p.m. |
Created at: March 27, 2026, 3:13 p.m.