Triple
T6737410
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lyudmila Pavlichenko |
E153990
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Pavlichenko
Pavlichenko is a Ukrainian surname most famously borne by Lyudmila Pavlichenko, a celebrated Soviet World War II sniper.
|
E615017
|
NE FINISHED |
How this triple was built (4 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: Pavlichenko | Statement: [Lyudmila Pavlichenko, familyName, Pavlichenko]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pavlichenko Context triple: [Lyudmila Pavlichenko, familyName, Pavlichenko]
-
A.
Vassili Zaitsev
Vassili Zaitsev is a legendary Soviet sniper of World War II, renowned for his exploits during the Battle of Stalingrad and later popularized in film and literature.
-
B.
Makarov
Makarov is a Russian surname most prominently associated with notable figures such as Hall of Fame ice hockey player Sergei Makarov.
-
C.
Strelkovka
Strelkovka is a rural locality in Russia best known as the birthplace of prominent Soviet military commander Georgy Zhukov.
-
D.
Shaposhnikov
Shaposhnikov is a Russian surname most notably associated with Soviet military leader Boris Shaposhnikov.
-
E.
Fedor Tokarev
Fedor Tokarev was a prominent Soviet firearms designer best known for creating influential weapons such as the Tokarev pistol and the SVT-40 semi-automatic rifle.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Pavlichenko Triple: [Lyudmila Pavlichenko, familyName, Pavlichenko]
Generated description
Pavlichenko is a Ukrainian surname most famously borne by Lyudmila Pavlichenko, a celebrated Soviet World War II sniper.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Pavlichenko Target entity description: Pavlichenko is a Ukrainian surname most famously borne by Lyudmila Pavlichenko, a celebrated Soviet World War II sniper.
-
A.
Vassili Zaitsev
Vassili Zaitsev is a legendary Soviet sniper of World War II, renowned for his exploits during the Battle of Stalingrad and later popularized in film and literature.
-
B.
Makarov
Makarov is a Russian surname most prominently associated with notable figures such as Hall of Fame ice hockey player Sergei Makarov.
-
C.
Strelkovka
Strelkovka is a rural locality in Russia best known as the birthplace of prominent Soviet military commander Georgy Zhukov.
-
D.
Shaposhnikov
Shaposhnikov is a Russian surname most notably associated with Soviet military leader Boris Shaposhnikov.
-
E.
Fedor Tokarev
Fedor Tokarev was a prominent Soviet firearms designer best known for creating influential weapons such as the Tokarev pistol and the SVT-40 semi-automatic rifle.
- F. None of above. chosen
Provenance (5 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_69c6880d84d8819095d19de2295f26ac |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d1850a288190aa7e647fefbb0ede |
completed | March 27, 2026, 6:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c70b0b97248190bf6bac160fe3d45b |
completed | March 27, 2026, 10:56 p.m. |
| NEDg | Description generation | batch_69c70bb0714c819094e80a2dfc960c99 |
completed | March 27, 2026, 10:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c70c51e0148190be64afb56690b34f |
completed | March 27, 2026, 11:01 p.m. |
Created at: March 27, 2026, 2:10 p.m.