Triple
T5892959
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anastasia (1997 film) |
E131033
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Anya
Anya is the spirited, amnesiac young woman in the animated film "Anastasia" who embarks on a journey to discover whether she is the lost Russian Grand Duchess.
|
E563376
|
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: Anya | Statement: [Anastasia (1997 film), mainCharacter, Anya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anya Context triple: [Anastasia (1997 film), mainCharacter, Anya]
-
A.
Anya
Anya is a person known primarily through her relationship to someone named Hannah, likely as a friend or family member.
-
B.
Anya
Anya is the given name of actress Anya Taylor-Joy, known for her roles in films like "The Witch" and the series "The Queen's Gambit."
-
C.
Natalya
Natalya is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and derived from the Latin name Natalia.
-
D.
Yelena
Yelena is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and equivalent to Helen or Helena in English.
-
E.
Sonya
Sonya is a gentle, selfless young woman in Leo Tolstoy’s novel "War and Peace," known for her unrequited love and quiet loyalty to the Rostov family.
- 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: Anya Triple: [Anastasia (1997 film), mainCharacter, Anya]
Generated description
Anya is the spirited, amnesiac young woman in the animated film "Anastasia" who embarks on a journey to discover whether she is the lost Russian Grand Duchess.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Anya Target entity description: Anya is the spirited, amnesiac young woman in the animated film "Anastasia" who embarks on a journey to discover whether she is the lost Russian Grand Duchess.
-
A.
Anya
Anya is a person known primarily through her relationship to someone named Hannah, likely as a friend or family member.
-
B.
Anya
Anya is the given name of actress Anya Taylor-Joy, known for her roles in films like "The Witch" and the series "The Queen's Gambit."
-
C.
Natalya
Natalya is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and derived from the Latin name Natalia.
-
D.
Yelena
Yelena is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and equivalent to Helen or Helena in English.
-
E.
Sonya
Sonya is a gentle, selfless young woman in Leo Tolstoy’s novel "War and Peace," known for her unrequited love and quiet loyalty to the Rostov family.
- 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_69c00857439c819095950754176aa58a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c036b5c68481909fdcba428238c74d |
completed | March 22, 2026, 6:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c1134bb82881908b912f96a3b6f0f1 |
completed | March 23, 2026, 10:17 a.m. |
| NEDg | Description generation | batch_69c113c9bc048190ab517300d56dd8e0 |
completed | March 23, 2026, 10:19 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c1144e77f881908ab59a67160c1630 |
completed | March 23, 2026, 10:22 a.m. |
Created at: March 22, 2026, 3:58 p.m.