Triple
T20493284
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Caer en tentación |
E502801
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object |
Erika de la Rosa
Erika de la Rosa is a Mexican actress known for her work in telenovelas and television dramas.
|
E1521085
|
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: Erika de la Rosa | Statement: [Caer en tentación, castMember, Erika de la Rosa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Erika de la Rosa Context triple: [Caer en tentación, castMember, Erika de la Rosa]
-
A.
Erika Flores
Erika Flores is an American actress best known for her role as Colleen Cooper on the television series "Dr. Quinn, Medicine Woman."
-
B.
Sofia Arreguin
Sofia Arreguin is a member of the creative collective or group known as Wand.
-
C.
Brisa Carrillo
Brisa Carrillo is a Mexican actress and singer best known for her work in telenovelas and youth-oriented television series.
-
D.
Victoria Villarruel
Victoria Villarruel is an Argentine lawyer and politician known for her conservative stance on human rights issues and for serving as the country’s vice president alongside President Javier Milei.
-
E.
Silvia Lemus
Silvia Lemus is a Mexican journalist and television host best known as the longtime wife and intellectual partner of celebrated writer Carlos Fuentes.
- 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: Erika de la Rosa Triple: [Caer en tentación, castMember, Erika de la Rosa]
Generated description
Erika de la Rosa is a Mexican actress known for her work in telenovelas and television dramas.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Erika de la Rosa Target entity description: Erika de la Rosa is a Mexican actress known for her work in telenovelas and television dramas.
-
A.
Erika Flores
Erika Flores is an American actress best known for her role as Colleen Cooper on the television series "Dr. Quinn, Medicine Woman."
-
B.
Sofia Arreguin
Sofia Arreguin is a member of the creative collective or group known as Wand.
-
C.
Brisa Carrillo
Brisa Carrillo is a Mexican actress and singer best known for her work in telenovelas and youth-oriented television series.
-
D.
Victoria Villarruel
Victoria Villarruel is an Argentine lawyer and politician known for her conservative stance on human rights issues and for serving as the country’s vice president alongside President Javier Milei.
-
E.
Silvia Lemus
Silvia Lemus is a Mexican journalist and television host best known as the longtime wife and intellectual partner of celebrated writer Carlos Fuentes.
- 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_69e0b4b0373881909dd3e9387f82eab4 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e69cbb3bd081909351525208b41bba |
completed | April 20, 2026, 9:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0a96dfbd5481908eaaa45696e5b09e |
completed | May 18, 2026, 4:34 a.m. |
| NEDg | Description generation | batch_6a0a980276bc81908e9f6c6596880a8d |
completed | May 18, 2026, 4:39 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0a986105bc819082556e8dec156ab6 |
completed | May 18, 2026, 4:41 a.m. |
Created at: April 16, 2026, 11:35 a.m.