Triple
T13716666
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Two Women |
E328918
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object |
Anna Astrakhantseva
Anna Astrakhantseva is an actress known for her role in the film "Two Women."
|
E1086601
|
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: Anna Astrakhantseva | Statement: [Two Women, hasCastMember, Anna Astrakhantseva]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anna Astrakhantseva Context triple: [Two Women, hasCastMember, Anna Astrakhantseva]
-
A.
Tatyana Lioznova
Tatyana Lioznova was a Soviet film and television director best known for her influential spy drama works and contributions to Russian cinema.
-
B.
Irina Skobtseva
Irina Skobtseva was a Soviet and Russian actress known for her roles in classic films such as "War and Peace" and "Walking the Streets of Moscow."
-
C.
Anna Krylova
Anna Krylova was the wife of Nobel Prize–winning Soviet physicist Peter Kapitza.
-
D.
Ludmila Bragina
Ludmila Bragina is a former Soviet middle-distance runner best known for winning Olympic gold and setting multiple world records in the 1500 metres in the early 1970s.
-
E.
Zoya Boguslavskaya
Zoya Boguslavskaya is a Russian writer, literary critic, and cultural figure known for her work in contemporary literature and her involvement in Moscow’s artistic circles.
- 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: Anna Astrakhantseva Triple: [Two Women, hasCastMember, Anna Astrakhantseva]
Generated description
Anna Astrakhantseva is an actress known for her role in the film "Two Women."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Anna Astrakhantseva Target entity description: Anna Astrakhantseva is an actress known for her role in the film "Two Women."
-
A.
Tatyana Lioznova
Tatyana Lioznova was a Soviet film and television director best known for her influential spy drama works and contributions to Russian cinema.
-
B.
Irina Skobtseva
Irina Skobtseva was a Soviet and Russian actress known for her roles in classic films such as "War and Peace" and "Walking the Streets of Moscow."
-
C.
Anna Krylova
Anna Krylova was the wife of Nobel Prize–winning Soviet physicist Peter Kapitza.
-
D.
Ludmila Bragina
Ludmila Bragina is a former Soviet middle-distance runner best known for winning Olympic gold and setting multiple world records in the 1500 metres in the early 1970s.
-
E.
Zoya Boguslavskaya
Zoya Boguslavskaya is a Russian writer, literary critic, and cultural figure known for her work in contemporary literature and her involvement in Moscow’s artistic circles.
- 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_69d80770b9bc81909f70c8c317d53cff |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dd4398f0448190810c840a82228706 |
completed | April 13, 2026, 7:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd192957008190b525778430b56ca0 |
completed | May 7, 2026, 10:58 p.m. |
| NEDg | Description generation | batch_69fd1fb29ef88190bfa15c163ca392ed |
completed | May 7, 2026, 11:26 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd2006221081908ab46e1eadb52e3d |
completed | May 7, 2026, 11:28 p.m. |
Created at: April 9, 2026, 9:54 p.m.