Triple
T14658646
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Bamba |
E344176
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object |
Danielle von Zerneck
Danielle von Zerneck is an American actress and producer best known for playing Donna Ludwig in the 1987 biographical film "La Bamba."
|
E1287190
|
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: Danielle von Zerneck | Statement: [La Bamba, stars, Danielle von Zerneck]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Danielle von Zerneck Context triple: [La Bamba, stars, Danielle von Zerneck]
-
A.
Danielle Lassner
Danielle Lassner is the mother of television producer Andy Lassner, known for his work on "The Ellen DeGeneres Show."
-
B.
Danielle Judovits
Danielle Judovits is an American voice actress known for her work in animated television series and video games.
-
C.
Nicole Kruspe
Nicole Kruspe is a linguist known for her extensive research and documentation of Aslian languages spoken by indigenous communities in the Malay Peninsula.
-
D.
Melanie Nissen
Melanie Nissen is a music industry figure best known as the co-founder of the influential Los Angeles punk label Slash Records.
-
E.
Danielle Savre
Danielle Savre is an American actress best known for her starring role as firefighter Maya Bishop on the television drama series "Station 19."
- 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: Danielle von Zerneck Triple: [La Bamba, stars, Danielle von Zerneck]
Generated description
Danielle von Zerneck is an American actress and producer best known for playing Donna Ludwig in the 1987 biographical film "La Bamba."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Danielle von Zerneck Target entity description: Danielle von Zerneck is an American actress and producer best known for playing Donna Ludwig in the 1987 biographical film "La Bamba."
-
A.
Danielle Lassner
Danielle Lassner is the mother of television producer Andy Lassner, known for his work on "The Ellen DeGeneres Show."
-
B.
Danielle Judovits
Danielle Judovits is an American voice actress known for her work in animated television series and video games.
-
C.
Nicole Kruspe
Nicole Kruspe is a linguist known for her extensive research and documentation of Aslian languages spoken by indigenous communities in the Malay Peninsula.
-
D.
Melanie Nissen
Melanie Nissen is a music industry figure best known as the co-founder of the influential Los Angeles punk label Slash Records.
-
E.
Danielle Savre
Danielle Savre is an American actress best known for her starring role as firefighter Maya Bishop on the television drama series "Station 19."
- 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_69d822e283fc8190a0e4c235cf880052 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb51b6a248190a44050c0e0ec2d16 |
completed | April 14, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a02ef9f91c0819080931a4f21546915 |
completed | May 12, 2026, 9:15 a.m. |
| NEDg | Description generation | batch_6a02f113a41c8190b17611eaa50516ca |
completed | May 12, 2026, 9:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a02f1e980f4819087eb191e13396581 |
completed | May 12, 2026, 9:24 a.m. |
Created at: April 10, 2026, 1:27 a.m.