Triple
T2461349
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Manchester by the Sea |
E54540
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Lauren Beck
Lauren Beck is a film producer best known for her work on the critically acclaimed drama "Manchester by the Sea."
|
E288898
|
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: Lauren Beck | Statement: [Manchester by the Sea, producer, Lauren Beck]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lauren Beck Context triple: [Manchester by the Sea, producer, Lauren Beck]
-
A.
Lauren Barber
Lauren Barber is best known as the wife of English musician and actor Gary Kemp.
-
B.
Lauren Nourse
Lauren Nourse is an Australian former netball player best known for her career with the Queensland Firebirds in the ANZ Championship.
-
C.
Kate Beahan
Kate Beahan is an Australian actress known for her roles in films such as "Flightplan" and "The Wicker Man."
-
D.
Rebekkah Brunson
Rebekkah Brunson is a former WNBA forward renowned for her dominant rebounding, multiple championships, and All-Star career, primarily with the Minnesota Lynx.
-
E.
Lauren Cox
Lauren Cox is an American basketball player best known as a standout forward for Baylor University, where she helped lead the Lady Bears to an NCAA championship.
- 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: Lauren Beck Triple: [Manchester by the Sea, producer, Lauren Beck]
Generated description
Lauren Beck is a film producer best known for her work on the critically acclaimed drama "Manchester by the Sea."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lauren Beck Target entity description: Lauren Beck is a film producer best known for her work on the critically acclaimed drama "Manchester by the Sea."
-
A.
Lauren Barber
Lauren Barber is best known as the wife of English musician and actor Gary Kemp.
-
B.
Lauren Nourse
Lauren Nourse is an Australian former netball player best known for her career with the Queensland Firebirds in the ANZ Championship.
-
C.
Kate Beahan
Kate Beahan is an Australian actress known for her roles in films such as "Flightplan" and "The Wicker Man."
-
D.
Rebekkah Brunson
Rebekkah Brunson is a former WNBA forward renowned for her dominant rebounding, multiple championships, and All-Star career, primarily with the Minnesota Lynx.
-
E.
Lauren Cox
Lauren Cox is an American basketball player best known as a standout forward for Baylor University, where she helped lead the Lady Bears to an NCAA championship.
- 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_69ab49dee84c819096b50a0049c347ac |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd11c47408190b10c7f6a151f2db2 |
completed | March 7, 2026, 7:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afa04046cc81908876e639d2144d8f |
completed | March 10, 2026, 4:38 a.m. |
| NEDg | Description generation | batch_69afa180fadc8190b376687c8afb1748 |
completed | March 10, 2026, 4:43 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69afa2172bc881908e17ab0eb3f9bb08 |
completed | March 10, 2026, 4:46 a.m. |
Created at: March 6, 2026, 9:44 p.m.