Triple
T613164
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frances |
E12143
|
entity |
| Predicate | hasNotableBearerExample |
P458
|
FINISHED |
| Object |
Frances McDormand
Frances McDormand is an acclaimed American actress known for her powerful, understated performances in films such as "Fargo," "Three Billboards Outside Ebbing, Missouri," and "Nomadland," for which she has won multiple Academy Awards.
|
E76562
|
NE FINISHED |
How this triple was built (5 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: Frances McDormand | Statement: [Frances, hasNotableBearerExample, Frances McDormand]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Frances McDormand Context triple: [Frances, hasNotableBearerExample, Frances McDormand]
-
A.
Julianne Moore
Julianne Moore is an acclaimed American actress known for her emotionally nuanced performances in both independent films and major studio productions, including multiple Oscar-nominated roles and a Best Actress win for "Still Alice."
-
B.
Melissa Leo
Melissa Leo is an American actress acclaimed for her powerful character roles in film and television, including her Oscar-winning performance in "The Fighter."
-
C.
Laura Linney
Laura Linney is an American actress acclaimed for her versatile performances in film, television, and theater, with notable roles in works such as "You Can Count on Me," "The Truman Show," and the series "Ozark."
-
D.
Patricia Arquette
Patricia Arquette is an American actress acclaimed for her versatile film and television roles, including award-winning performances in projects like "Medium" and "Boyhood."
-
E.
Tilda Swinton
Tilda Swinton is a British actress known for her chameleonic performances in independent and mainstream films, often portraying unconventional and androgynous characters.
- 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: Frances McDormand Triple: [Frances, hasNotableBearerExample, Frances McDormand]
Generated description
Frances McDormand is an acclaimed American actress known for her powerful, understated performances in films such as "Fargo," "Three Billboards Outside Ebbing, Missouri," and "Nomadland," for which she has won multiple Academy Awards.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Frances McDormand Target entity description: Frances McDormand is an acclaimed American actress known for her powerful, understated performances in films such as "Fargo," "Three Billboards Outside Ebbing, Missouri," and "Nomadland," for which she has won multiple Academy Awards.
-
A.
Julianne Moore
Julianne Moore is an acclaimed American actress known for her emotionally nuanced performances in both independent films and major studio productions, including multiple Oscar-nominated roles and a Best Actress win for "Still Alice."
-
B.
Melissa Leo
Melissa Leo is an American actress acclaimed for her powerful character roles in film and television, including her Oscar-winning performance in "The Fighter."
-
C.
Laura Linney
Laura Linney is an American actress acclaimed for her versatile performances in film, television, and theater, with notable roles in works such as "You Can Count on Me," "The Truman Show," and the series "Ozark."
-
D.
Patricia Arquette
Patricia Arquette is an American actress acclaimed for her versatile film and television roles, including award-winning performances in projects like "Medium" and "Boyhood."
-
E.
Tilda Swinton
Tilda Swinton is a British actress known for her chameleonic performances in independent and mainstream films, often portraying unconventional and androgynous characters.
- F. None of above. chosen
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableBearerExample Context triple: [Frances, hasNotableBearerExample, Frances McDormand]
-
A.
hasNotableBearer
chosen
Indicates that an entity (such as a name, title, or identifier) is borne by at least one notable person or entity.
-
B.
hasNotableFieldOfBearers
Indicates that the entities share a significant or distinguished area of activity, expertise, or achievement associated with their bearers.
-
C.
hasStandardBearer
Indicates that one entity serves as the official flag- or standard-carrier for another entity, typically in a ceremonial, military, or representative capacity.
-
D.
hasExample
Indicates that one entity serves as an instance, illustration, or concrete example of another entity.
-
E.
hasNotableBearerOccupation
Indicates that an entity is associated with a notable person who holds a specific occupation.
- F. None of above.
Provenance (6 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_69a493309df48190a327f748e88049a6 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49e08dbf88190ab050078a63e266b |
completed | March 1, 2026, 8:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a533dabe288190ab25bd6d76e79d06 |
completed | March 2, 2026, 6:53 a.m. |
| NEDg | Description generation | batch_69a54e4849f48190868d7b624e450dc3 |
completed | March 2, 2026, 8:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a55048e2ec81908d306f44b2ca24fa |
completed | March 2, 2026, 8:54 a.m. |
| PD | Predicate disambiguation | batch_69a49cfbcbf88190a854921dc531eba8 |
completed | March 1, 2026, 8:09 p.m. |
Created at: March 1, 2026, 7:35 p.m.