Triple
T1105442
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mila Kunis |
E25478
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
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."
|
E125468
|
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: Milena | Statement: [Mila Kunis, givenName, Milena]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Milena Context triple: [Mila Kunis, givenName, Milena]
-
A.
Maud
Maud is a feminine given name of Germanic origin, historically borne by European royalty and nobility.
-
B.
Valeria
Valeria was a Roman imperial princess and later empress, best known as the daughter of Emperor Diocletian and for her tragic fate during the political turmoil of the Tetrarchy.
-
C.
Eva Luna
Eva Luna is a novel by Chilean author Isabel Allende that follows the imaginative life story of a young Latin American woman against a backdrop of political and social upheaval.
-
D.
Eva
Eva is a feminine given name of Hebrew origin, equivalent to "Eve" and widely used in many languages and cultures.
-
E.
Helene
Helene is the given name of Leni Riefenstahl, the controversial German filmmaker and actress known for her propaganda films during the Nazi era.
- 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: Milena Triple: [Mila Kunis, givenName, Milena]
Generated description
Milena is the birth name of actress Mila Kunis, a Ukrainian-born American performer known for roles in "That '70s Show" and "Black Swan."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Milena Target entity description: Milena is the birth name of actress Mila Kunis, a Ukrainian-born American performer known for roles in "That '70s Show" and "Black Swan."
-
A.
Maud
Maud is a feminine given name of Germanic origin, historically borne by European royalty and nobility.
-
B.
Valeria
Valeria was a Roman imperial princess and later empress, best known as the daughter of Emperor Diocletian and for her tragic fate during the political turmoil of the Tetrarchy.
-
C.
Eva Luna
Eva Luna is a novel by Chilean author Isabel Allende that follows the imaginative life story of a young Latin American woman against a backdrop of political and social upheaval.
-
D.
Eva
Eva is a feminine given name of Hebrew origin, equivalent to "Eve" and widely used in many languages and cultures.
-
E.
Helene
Helene is the given name of Leni Riefenstahl, the controversial German filmmaker and actress known for her propaganda films during the Nazi era.
- 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_69a49428d4448190b3b36991ceae87ce |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4b9e21f048190bf4b63dd2c7c7641 |
completed | March 1, 2026, 10:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac4c4a402081908ee138257425a336 |
completed | March 7, 2026, 4:03 p.m. |
| NEDg | Description generation | batch_69ac4ccf989c8190aed2ff01ad6fbcab |
completed | March 7, 2026, 4:05 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac4d4cbed88190ac743160198493b2 |
completed | March 7, 2026, 4:07 p.m. |
Created at: March 1, 2026, 7:43 p.m.