Triple
T18352181
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cano |
E439691
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Laura Cano
Laura Cano is a notable individual recognized for achievements significant enough to be associated with the surname Cano.
|
E1376947
|
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: Laura Cano | Statement: [Cano, hasNotableBearer, Laura Cano]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laura Cano Context triple: [Cano, hasNotableBearer, Laura Cano]
-
A.
Ana Cano
Ana Cano is a notable individual recognized for achievements significant enough to be associated with the surname Cano.
-
B.
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.
-
C.
Ximena Lamadrid
Ximena Lamadrid is a Mexican actress known for her work in film and television, including roles in international productions.
-
D.
Selenis Leyva
Selenis Leyva is a Cuban-American actress best known for her role as Gloria Mendoza on the Netflix series "Orange Is the New Black."
-
E.
Carla Jimenez
Carla Jimenez is an American actress best known for her comedic television roles, including a main role on the sitcom "The Mick."
- 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: Laura Cano Triple: [Cano, hasNotableBearer, Laura Cano]
Generated description
Laura Cano is a notable individual recognized for achievements significant enough to be associated with the surname Cano.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Laura Cano Target entity description: Laura Cano is a notable individual recognized for achievements significant enough to be associated with the surname Cano.
-
A.
Ana Cano
Ana Cano is a notable individual recognized for achievements significant enough to be associated with the surname Cano.
-
B.
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.
-
C.
Ximena Lamadrid
Ximena Lamadrid is a Mexican actress known for her work in film and television, including roles in international productions.
-
D.
Selenis Leyva
Selenis Leyva is a Cuban-American actress best known for her role as Gloria Mendoza on the Netflix series "Orange Is the New Black."
-
E.
Carla Jimenez
Carla Jimenez is an American actress best known for her comedic television roles, including a main role on the sitcom "The Mick."
- 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_69d8b918221c8190a9f7b563d64ac677 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e514f8f07c8190a1506d4a327369d0 |
completed | April 19, 2026, 5:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a073b12c1c881908a0b6c056be81285 |
completed | May 15, 2026, 3:26 p.m. |
| NEDg | Description generation | batch_6a073c19c3b88190a6114d272ebd5fbc |
completed | May 15, 2026, 3:30 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a073d25ba448190a62777b68d2f3136 |
completed | May 15, 2026, 3:35 p.m. |
Created at: April 10, 2026, 10:37 a.m.