Triple
T9853746
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wilmer Valderrama |
E239532
|
entity |
| Predicate | partner |
P1136
|
FINISHED |
| Object |
Amanda Pacheco
Amanda Pacheco is an American model and divemaster who has gained public attention through her relationship with actor Wilmer Valderrama.
|
E838762
|
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: Amanda Pacheco | Statement: [Wilmer Valderrama, partner, Amanda Pacheco]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amanda Pacheco Context triple: [Wilmer Valderrama, partner, Amanda Pacheco]
-
A.
Lauren Vélez
Lauren Vélez is an American actress best known for her role as Lieutenant Maria LaGuerta on the television series "Dexter."
-
B.
Sofia Arreguin
Sofia Arreguin is a member of the creative collective or group known as Wand.
-
C.
Talisa Soto
Talisa Soto is an American actress and former model best known for her roles in films such as the James Bond movie "Licence to Kill" and the "Mortal Kombat" series.
-
D.
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.
-
E.
Chelsea Tavares
Chelsea Tavares is an American actress best known for her prominent role on the television drama series "All American."
- 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: Amanda Pacheco Triple: [Wilmer Valderrama, partner, Amanda Pacheco]
Generated description
Amanda Pacheco is an American model and divemaster who has gained public attention through her relationship with actor Wilmer Valderrama.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Amanda Pacheco Target entity description: Amanda Pacheco is an American model and divemaster who has gained public attention through her relationship with actor Wilmer Valderrama.
-
A.
Lauren Vélez
Lauren Vélez is an American actress best known for her role as Lieutenant Maria LaGuerta on the television series "Dexter."
-
B.
Sofia Arreguin
Sofia Arreguin is a member of the creative collective or group known as Wand.
-
C.
Talisa Soto
Talisa Soto is an American actress and former model best known for her roles in films such as the James Bond movie "Licence to Kill" and the "Mortal Kombat" series.
-
D.
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.
-
E.
Chelsea Tavares
Chelsea Tavares is an American actress best known for her prominent role on the television drama series "All American."
- 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_69ca84e4fdc08190a624425bcef98665 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb376d32c819089381cf6ed83629d |
completed | April 2, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d299c51ea08190902e03552fbe7ebb |
completed | April 5, 2026, 5:20 p.m. |
| NEDg | Description generation | batch_69d29b7430248190b8965eaf1286dd7c |
completed | April 5, 2026, 5:27 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d29c7ba9f081908f4614098d6c954b |
completed | April 5, 2026, 5:31 p.m. |
Created at: March 30, 2026, 8:34 p.m.