Triple
T16229191
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | American Me |
E393933
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object |
Sal Lopez
Sal Lopez is an American actor known for his work in film, television, and theater, often portraying complex Latino characters in dramas and crime stories.
|
E1264479
|
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: Sal Lopez | Statement: [American Me, hasCastMember, Sal Lopez]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sal Lopez Context triple: [American Me, hasCastMember, Sal Lopez]
-
A.
Luis Lopez
Luis Lopez is a film producer best known for his work on the acclaimed video game documentary "The King of Kong: A Fistful of Quarters."
-
B.
Danny Lopez
Danny Lopez is a former American professional boxer and world featherweight champion known for his formidable punching power and exciting, aggressive style in the ring.
-
C.
Tomas Lopez
Tomas Lopez was a historical figure known for founding the Colombian town of Chocontá.
-
D.
Frank Dominguez
Frank Dominguez is an entrepreneur best known as a founder of the cloud-based software company Salesforce.
-
E.
Frank Dominguez
Frank Dominguez is a person whose specific public background or notable achievements are not clearly identifiable from the given information.
- 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: Sal Lopez Triple: [American Me, hasCastMember, Sal Lopez]
Generated description
Sal Lopez is an American actor known for his work in film, television, and theater, often portraying complex Latino characters in dramas and crime stories.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sal Lopez Target entity description: Sal Lopez is an American actor known for his work in film, television, and theater, often portraying complex Latino characters in dramas and crime stories.
-
A.
Luis Lopez
Luis Lopez is a film producer best known for his work on the acclaimed video game documentary "The King of Kong: A Fistful of Quarters."
-
B.
Danny Lopez
Danny Lopez is a former American professional boxer and world featherweight champion known for his formidable punching power and exciting, aggressive style in the ring.
-
C.
Tomas Lopez
Tomas Lopez was a historical figure known for founding the Colombian town of Chocontá.
-
D.
Frank Dominguez
Frank Dominguez is an entrepreneur best known as a founder of the cloud-based software company Salesforce.
-
E.
Frank Dominguez
Frank Dominguez is a person whose specific public background or notable achievements are not clearly identifiable from the given information.
- 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_69d87f204df88190a8f88923decf9835 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e23d2889688190ac04e4e9479cabf4 |
completed | April 17, 2026, 2:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01953eaa6c819091f7d63a1e3e7070 |
completed | May 11, 2026, 8:37 a.m. |
| NEDg | Description generation | batch_6a019b79d7148190abf4b41f0c84c62e |
completed | May 11, 2026, 9:03 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a019c1b60f08190a6469602751e3471 |
completed | May 11, 2026, 9:06 a.m. |
Created at: April 10, 2026, 5:03 a.m.