Triple
T7592862
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | License to Kill |
E179781
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object |
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.
|
E690378
|
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: Talisa Soto | Statement: [License to Kill, stars, Talisa Soto]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Talisa Soto Context triple: [License to Kill, stars, Talisa Soto]
-
A.
Sofia Arreguin
Sofia Arreguin is a member of the creative collective or group known as Wand.
-
B.
Lola Salazar
Lola Salazar is a fictional character appearing in the narrative of *The Wolf Song*.
-
C.
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.
-
D.
Tatiana Gutierrez
Tatiana Gutierrez is a recurring nurse character in The Evil Within survival horror video game series, serving as a mysterious guide and save-point attendant for the protagonist within the STEM world.
-
E.
Lauren Vélez
Lauren Vélez is an American actress best known for her role as Lieutenant Maria LaGuerta on the television series "Dexter."
- 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: Talisa Soto Triple: [License to Kill, stars, Talisa Soto]
Generated description
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.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Talisa Soto Target entity description: 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.
-
A.
Sofia Arreguin
Sofia Arreguin is a member of the creative collective or group known as Wand.
-
B.
Lola Salazar
Lola Salazar is a fictional character appearing in the narrative of *The Wolf Song*.
-
C.
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.
-
D.
Tatiana Gutierrez
Tatiana Gutierrez is a recurring nurse character in The Evil Within survival horror video game series, serving as a mysterious guide and save-point attendant for the protagonist within the STEM world.
-
E.
Lauren Vélez
Lauren Vélez is an American actress best known for her role as Lieutenant Maria LaGuerta on the television series "Dexter."
- 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_69c69f3487ec8190bf7acdf2dd91e6d6 |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c6f9b92c348190b547f0aacfb8d6be |
completed | March 27, 2026, 9:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c934c199f08190a8fbb7f3c6f5464c |
completed | March 29, 2026, 2:18 p.m. |
| NEDg | Description generation | batch_69c93547255c81909a56ff28da51c3c4 |
completed | March 29, 2026, 2:20 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c9359dd42c8190929f204c11194041 |
completed | March 29, 2026, 2:22 p.m. |
Created at: March 27, 2026, 3:53 p.m.