Triple
T11956618
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lauren Heller |
E284568
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object |
Molly Bernard
Molly Bernard is an American actress best known for her role as Lauren Heller on the television series "Younger."
|
E957828
|
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: Molly Bernard | Statement: [Lauren Heller, portrayedBy, Molly Bernard]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Molly Bernard Context triple: [Lauren Heller, portrayedBy, Molly Bernard]
-
A.
Molly Messick
Molly Messick is an American audio producer and journalist known for her work in public radio and podcasting.
-
B.
Molly Phillips
Molly Phillips is a fictional character best known as the widowed musician mother and paranormal investigator in the Disney Channel series "So Weird."
-
C.
Melissa Bernstein
Melissa Bernstein is an American television and film producer best known for her work on acclaimed series such as Breaking Bad and Better Call Saul, as well as their related projects.
-
D.
Molly O'Neil
Molly O'Neil is the daughter of American actress and comedian Teri Garr.
-
E.
Molly Cahill
Molly Cahill is a young carnival performer and love interest in William Lindsay Gresham’s noir novel "Nightmare Alley," often portrayed as a symbol of innocence amid the story’s corruption and deceit.
- 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: Molly Bernard Triple: [Lauren Heller, portrayedBy, Molly Bernard]
Generated description
Molly Bernard is an American actress best known for her role as Lauren Heller on the television series "Younger."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Molly Bernard Target entity description: Molly Bernard is an American actress best known for her role as Lauren Heller on the television series "Younger."
-
A.
Molly Messick
Molly Messick is an American audio producer and journalist known for her work in public radio and podcasting.
-
B.
Molly Phillips
Molly Phillips is a fictional character best known as the widowed musician mother and paranormal investigator in the Disney Channel series "So Weird."
-
C.
Melissa Bernstein
Melissa Bernstein is an American television and film producer best known for her work on acclaimed series such as Breaking Bad and Better Call Saul, as well as their related projects.
-
D.
Molly O'Neil
Molly O'Neil is the daughter of American actress and comedian Teri Garr.
-
E.
Molly Cahill
Molly Cahill is a young carnival performer and love interest in William Lindsay Gresham’s noir novel "Nightmare Alley," often portrayed as a symbol of innocence amid the story’s corruption and deceit.
- 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_69d6ab2db38c8190b1f0ed6663ef8ada |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d90366fda8819083168c93abad27d4 |
completed | April 10, 2026, 2:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f471c931a88190a9d29262c62b9472 |
completed | May 1, 2026, 9:26 a.m. |
| NEDg | Description generation | batch_69f47b7ac4048190ae09f18f1a90338f |
completed | May 1, 2026, 10:07 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f47db91f38819092b7b5c5e2bb489b |
completed | May 1, 2026, 10:17 a.m. |
Created at: April 8, 2026, 9:45 p.m.