Triple
T9789857
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chef (2014 film) |
E237579
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Molly Allen
Molly Allen is a film producer known for her work on the 2014 comedy-drama "Chef."
|
E861218
|
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 Allen | Statement: [Chef (2014 film), producer, Molly Allen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Molly Allen Context triple: [Chef (2014 film), producer, Molly Allen]
-
A.
Molly Cregg
Molly Cregg is a minor character in the television series "The West Wing," known as the young niece of White House Press Secretary C. J. Cregg.
-
B.
Molly Smith Metzler
Molly Smith Metzler is an American playwright, screenwriter, and television producer best known for creating the acclaimed Netflix miniseries "Maid."
-
C.
Molly Smith
Molly Smith is a daughter of FedEx founder and CEO Frederick W. Smith.
-
D.
Molly Smith
Molly Smith is an American film producer known for her work on acclaimed movies such as the crime thriller "Sicario."
-
E.
Lisa McDowell
Lisa McDowell is the intelligent, independent love interest of Prince Akeem in the 1988 comedy film "Coming to America," known for challenging social expectations and valuing character over wealth.
- 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 Allen Triple: [Chef (2014 film), producer, Molly Allen]
Generated description
Molly Allen is a film producer known for her work on the 2014 comedy-drama "Chef."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Molly Allen Target entity description: Molly Allen is a film producer known for her work on the 2014 comedy-drama "Chef."
-
A.
Molly Cregg
Molly Cregg is a minor character in the television series "The West Wing," known as the young niece of White House Press Secretary C. J. Cregg.
-
B.
Molly Smith Metzler
Molly Smith Metzler is an American playwright, screenwriter, and television producer best known for creating the acclaimed Netflix miniseries "Maid."
-
C.
Molly Smith
Molly Smith is a daughter of FedEx founder and CEO Frederick W. Smith.
-
D.
Molly Smith
Molly Smith is an American film producer known for her work on acclaimed movies such as the crime thriller "Sicario."
-
E.
Lisa McDowell
Lisa McDowell is the intelligent, independent love interest of Prince Akeem in the 1988 comedy film "Coming to America," known for challenging social expectations and valuing character over wealth.
- 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_69ca84dc04488190b9c91193976c0960 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda214875481909f39e1d4dbac1fdb |
completed | April 1, 2026, 10:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d7faf8c1048190a136289a44a0930b |
completed | April 9, 2026, 7:16 p.m. |
| NEDg | Description generation | batch_69d81c40dc6081908cc186ee6cd0814e |
completed | April 9, 2026, 9:38 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d8277fbf0881908a1e16d6c07886e6 |
completed | April 9, 2026, 10:26 p.m. |
Created at: March 30, 2026, 8:28 p.m.