Triple
T16147734
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anamaria Marinca |
E391828
|
entity |
| Predicate | hasRole |
P161
|
FINISHED |
| Object |
Irena in The Countess
Irena in *The Countess* is a character portrayed by Romanian actress Anamaria Marinca in the historical horror film about the life of Countess Elizabeth Báthory.
|
E1196982
|
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: Irena in The Countess | Statement: [Anamaria Marinca, hasRole, Irena in The Countess]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Irena in The Countess Context triple: [Anamaria Marinca, hasRole, Irena in The Countess]
-
A.
Malena
Malena is a feminine given name, commonly used in various cultures and often considered a diminutive or variant of names like Magdalena.
-
B.
Milena
Milena is a small town and comune in the Province of Caltanissetta in central Sicily, Italy.
-
C.
Milena
Milena is the birth name of actress Mila Kunis, a Ukrainian-born American performer known for roles in "That '70s Show" and "Black Swan."
-
D.
Mrs. Levenstein
Mrs. Levenstein is a recurring comedic character in the American Pie film series, known as the supportive and often humorous wife of Noah Levenstein.
-
E.
Eileen
Eileen is a comic opera by composer Victor Herbert, known for its romantic Irish setting and melodic score.
- 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: Irena in The Countess Triple: [Anamaria Marinca, hasRole, Irena in The Countess]
Generated description
Irena in *The Countess* is a character portrayed by Romanian actress Anamaria Marinca in the historical horror film about the life of Countess Elizabeth Báthory.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Irena in The Countess Target entity description: Irena in *The Countess* is a character portrayed by Romanian actress Anamaria Marinca in the historical horror film about the life of Countess Elizabeth Báthory.
-
A.
Malena
Malena is a feminine given name, commonly used in various cultures and often considered a diminutive or variant of names like Magdalena.
-
B.
Milena
Milena is a small town and comune in the Province of Caltanissetta in central Sicily, Italy.
-
C.
Milena
Milena is the birth name of actress Mila Kunis, a Ukrainian-born American performer known for roles in "That '70s Show" and "Black Swan."
-
D.
Mrs. Levenstein
Mrs. Levenstein is a recurring comedic character in the American Pie film series, known as the supportive and often humorous wife of Noah Levenstein.
-
E.
Eileen
Eileen is a comic opera by composer Victor Herbert, known for its romantic Irish setting and melodic score.
- 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_69d87f1c65e48190aa2b4c472e9bafc4 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e21d9551e081908391061b092ff31b |
completed | April 17, 2026, 11:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fff7a7dc3481909f933acd72d6feff |
completed | May 10, 2026, 3:12 a.m. |
| NEDg | Description generation | batch_69fff81bb0008190947eeff64dc8eb88 |
completed | May 10, 2026, 3:14 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fff8905ce48190b99f8cc3504efba2 |
completed | May 10, 2026, 3:16 a.m. |
Created at: April 10, 2026, 5:01 a.m.