Triple
T6222425
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dexter |
E139146
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object |
Lauren Vélez
Lauren Vélez is an American actress best known for her role as Lieutenant Maria LaGuerta on the television series "Dexter."
|
E589513
|
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: Lauren Vélez | Statement: [Dexter, castMember, Lauren Vélez]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lauren Vélez Context triple: [Dexter, castMember, Lauren Vélez]
-
A.
Sofia Arreguin
Sofia Arreguin is a member of the creative collective or group known as Wand.
-
B.
Celina Carvajal
Celina Carvajal, also known professionally as Lena Hall, is a Tony Award–winning American actress and singer best known for her work in Broadway musicals and rock-inspired performances.
-
C.
Natalie Figueroa
Natalie Figueroa is a fictional prison administrator and later warden in the television series "Orange Is the New Black."
-
D.
Jossalyn Romo
Jossalyn Romo is known as the sister of former Dallas Cowboys quarterback and NFL broadcaster Tony Romo.
-
E.
Angelica Fuentes
Angelica Fuentes is a Mexican businesswoman and philanthropist known for her leadership roles in the energy sector and in professional soccer, as well as her advocacy for women's empowerment in Latin America.
- 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: Lauren Vélez Triple: [Dexter, castMember, Lauren Vélez]
Generated description
Lauren Vélez is an American actress best known for her role as Lieutenant Maria LaGuerta on the television series "Dexter."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lauren Vélez Target entity description: Lauren Vélez is an American actress best known for her role as Lieutenant Maria LaGuerta on the television series "Dexter."
-
A.
Sofia Arreguin
Sofia Arreguin is a member of the creative collective or group known as Wand.
-
B.
Celina Carvajal
Celina Carvajal, also known professionally as Lena Hall, is a Tony Award–winning American actress and singer best known for her work in Broadway musicals and rock-inspired performances.
-
C.
Natalie Figueroa
Natalie Figueroa is a fictional prison administrator and later warden in the television series "Orange Is the New Black."
-
D.
Jossalyn Romo
Jossalyn Romo is known as the sister of former Dallas Cowboys quarterback and NFL broadcaster Tony Romo.
-
E.
Angelica Fuentes
Angelica Fuentes is a Mexican businesswoman and philanthropist known for her leadership roles in the energy sector and in professional soccer, as well as her advocacy for women's empowerment in Latin America.
- 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_69c008aecb0c81909984b48f733ce8ae |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c062bddb688190add53172a7445d01 |
completed | March 22, 2026, 9:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c638568068819097811baeb8bf3ab3 |
completed | March 27, 2026, 7:57 a.m. |
| NEDg | Description generation | batch_69c638c52dd481909291cee6956ee4c9 |
completed | March 27, 2026, 7:59 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c63917cd5c8190bf6b257b27a7c2b9 |
completed | March 27, 2026, 8 a.m. |
Created at: March 22, 2026, 4:22 p.m.