Triple
T6486862
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ellen Barkin |
E146533
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Barkin
Barkin is the surname of American actress and producer Ellen Barkin, known for her intense performances in film, television, and theater.
|
E595998
|
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: Barkin | Statement: [Ellen Barkin, familyName, Barkin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Barkin Context triple: [Ellen Barkin, familyName, Barkin]
-
A.
Barkin Ladi
Barkin Ladi is a town and local government area in Plateau State, central Nigeria, known for its Berom population and highland agricultural landscape.
-
B.
Barkley
Barkley is a surname most notably associated with Alben W. Barkley, the 35th vice president of the United States under President Harry S. Truman.
-
C.
Shep
Shep is the station code used to identify Sheppard–Yonge station in the Toronto subway system.
-
D.
Barcy
Barcy is a small French commune located in the Île-de-France region, within the administrative area of Mitry-Mory in the Seine-et-Marne department.
-
E.
Norbit
Norbit is a 2007 American comedy film starring Eddie Murphy in multiple roles, known for its broad humor, heavy use of prosthetic makeup, and mixed critical reception.
- 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: Barkin Triple: [Ellen Barkin, familyName, Barkin]
Generated description
Barkin is the surname of American actress and producer Ellen Barkin, known for her intense performances in film, television, and theater.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Barkin Target entity description: Barkin is the surname of American actress and producer Ellen Barkin, known for her intense performances in film, television, and theater.
-
A.
Barkin Ladi
Barkin Ladi is a town and local government area in Plateau State, central Nigeria, known for its Berom population and highland agricultural landscape.
-
B.
Barkley
Barkley is a surname most notably associated with Alben W. Barkley, the 35th vice president of the United States under President Harry S. Truman.
-
C.
Shep
Shep is the station code used to identify Sheppard–Yonge station in the Toronto subway system.
-
D.
Barcy
Barcy is a small French commune located in the Île-de-France region, within the administrative area of Mitry-Mory in the Seine-et-Marne department.
-
E.
Norbit
Norbit is a 2007 American comedy film starring Eddie Murphy in multiple roles, known for its broad humor, heavy use of prosthetic makeup, and mixed critical reception.
- 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_69c0090158c08190af0df9a2348d2d52 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c06a706d4c8190b7a3cc8855abcecb |
completed | March 22, 2026, 10:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c653b4e91c81908dfa1798a057b21a |
completed | March 27, 2026, 9:53 a.m. |
| NEDg | Description generation | batch_69c65463dad88190ad2429140623ff80 |
completed | March 27, 2026, 9:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c654d0c2948190a4d586071133d759 |
completed | March 27, 2026, 9:58 a.m. |
Created at: March 22, 2026, 4:52 p.m.