Triple
T16686801
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lainie Kazan |
E405481
|
entity |
| Predicate | hasChild |
P369
|
FINISHED |
| Object |
Jennifer Bena
Jennifer Bena is the daughter of American actress and singer Lainie Kazan.
|
E1315636
|
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: Jennifer Bena | Statement: [Lainie Kazan, hasChild, Jennifer Bena]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jennifer Bena Context triple: [Lainie Kazan, hasChild, Jennifer Bena]
-
A.
Emily Benetto
Emily Benetto is the financially struggling young woman who turns to credit card fraud and increasingly dangerous criminal schemes in the crime thriller film "Emily the Criminal."
-
B.
Laura Yacobi Bateman
Laura Yacobi Bateman is known primarily as the wife of biblical scholar and theologian Herbert H. Bateman.
-
C.
Jorja Curtright
Jorja Curtright was an American actress and novelist best known for her work in mid-20th-century film and television and for being married to writer Sidney Sheldon.
-
D.
Tanya Biank
Tanya Biank is an American journalist and author known for her in-depth reporting and books on the lives and challenges of military families.
-
E.
Veanne Cox
Veanne Cox is an American actress and singer known for her work in musical theatre, film, and television, often recognized for her comedic and character roles.
- 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: Jennifer Bena Triple: [Lainie Kazan, hasChild, Jennifer Bena]
Generated description
Jennifer Bena is the daughter of American actress and singer Lainie Kazan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jennifer Bena Target entity description: Jennifer Bena is the daughter of American actress and singer Lainie Kazan.
-
A.
Emily Benetto
Emily Benetto is the financially struggling young woman who turns to credit card fraud and increasingly dangerous criminal schemes in the crime thriller film "Emily the Criminal."
-
B.
Laura Yacobi Bateman
Laura Yacobi Bateman is known primarily as the wife of biblical scholar and theologian Herbert H. Bateman.
-
C.
Jorja Curtright
Jorja Curtright was an American actress and novelist best known for her work in mid-20th-century film and television and for being married to writer Sidney Sheldon.
-
D.
Tanya Biank
Tanya Biank is an American journalist and author known for her in-depth reporting and books on the lives and challenges of military families.
-
E.
Veanne Cox
Veanne Cox is an American actress and singer known for her work in musical theatre, film, and television, often recognized for her comedic and character roles.
- 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_69d8838c28748190b3f5967c743940ab |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e37ea63b7081908a055036172f9683 |
completed | April 18, 2026, 12:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a03b3daaa6c819093420d0a4dc97a5a |
completed | May 12, 2026, 11:12 p.m. |
| NEDg | Description generation | batch_6a03b5109b108190a3dea7e6f44a060f |
completed | May 12, 2026, 11:17 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a03b65c8d5c819080b8b6b95aea8a35 |
completed | May 12, 2026, 11:23 p.m. |
Created at: April 10, 2026, 5:19 a.m.