Triple
T20493283
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Caer en tentación |
E502801
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object |
Ela Velden
Ela Velden is a Mexican actress and model known for her roles in contemporary telenovelas and television dramas.
|
E1434820
|
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: Ela Velden | Statement: [Caer en tentación, castMember, Ela Velden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ela Velden Context triple: [Caer en tentación, castMember, Ela Velden]
-
A.
Velda
Velda is the loyal and resourceful secretary and love interest of private investigator Mike Hammer in the hardboiled crime novel and film "Kiss Me Deadly."
-
B.
Evileen
Evileen is a song by the Southern rock band Atlanta Rhythm Section from their 1978 album "Champagne Jam."
-
C.
Dalva
Dalva is a surname most notably associated with American film editor Robert Dalva, recognized for his work on major Hollywood productions.
-
D.
Dalva
Dalva is a 1988 novel by American author Jim Harrison that follows a middle-aged woman’s journey through memory, loss, and family history on the Great Plains.
-
E.
Salka Valka
Salka Valka is a socially conscious novel by Icelandic Nobel laureate Halldór Laxness that portrays the struggles of a young woman and a fishing village amid poverty, class conflict, and political awakening.
- 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: Ela Velden Triple: [Caer en tentación, castMember, Ela Velden]
Generated description
Ela Velden is a Mexican actress and model known for her roles in contemporary telenovelas and television dramas.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ela Velden Target entity description: Ela Velden is a Mexican actress and model known for her roles in contemporary telenovelas and television dramas.
-
A.
Velda
Velda is the loyal and resourceful secretary and love interest of private investigator Mike Hammer in the hardboiled crime novel and film "Kiss Me Deadly."
-
B.
Evileen
Evileen is a song by the Southern rock band Atlanta Rhythm Section from their 1978 album "Champagne Jam."
-
C.
Dalva
Dalva is a surname most notably associated with American film editor Robert Dalva, recognized for his work on major Hollywood productions.
-
D.
Dalva
Dalva is a 1988 novel by American author Jim Harrison that follows a middle-aged woman’s journey through memory, loss, and family history on the Great Plains.
-
E.
Salka Valka
Salka Valka is a socially conscious novel by Icelandic Nobel laureate Halldór Laxness that portrays the struggles of a young woman and a fishing village amid poverty, class conflict, and political awakening.
- 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_6a0893b8a86081908783a9b8cf2dc485 |
completed | May 16, 2026, 3:56 p.m. |
| NEDg | Description generation | batch_6a08962f4e3881908df0bb55b6239c86 |
completed | May 16, 2026, 4:07 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0896b417bc8190963b69f84ea0e3c8 |
completed | May 16, 2026, 4:09 p.m. |
Created at: April 16, 2026, 11:35 a.m.