Triple
T19982352
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Law & Order: UK |
E493846
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object |
Georgia Taylor
Georgia Taylor is an English actress best known for her roles in television dramas such as Law & Order: UK and Coronation Street.
|
E1405702
|
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: Georgia Taylor | Statement: [Law & Order: UK, stars, Georgia Taylor]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Georgia Taylor Context triple: [Law & Order: UK, stars, Georgia Taylor]
-
A.
Mary Elizabeth Taylor
Mary Elizabeth Taylor was a daughter of U.S. President Zachary Taylor and a member of the prominent Taylor family in early 19th-century America.
-
B.
Adelaide Taylor
Adelaide Taylor is the daughter of Australian actress and director Rachel Griffiths.
-
C.
Harlem Taylor
Harlem Taylor is the son of American rapper and West Coast hip hop artist The Game.
-
D.
Georgia Davis
Georgia Davis was the second wife of American comedian and actor Red Skelton, with whom she shared both personal and professional aspects of his entertainment career.
-
E.
Georgia Thomas
Georgia Thomas is a fictional attorney and partner at the Boston law firm in the television series "Ally McBeal."
- 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: Georgia Taylor Triple: [Law & Order: UK, stars, Georgia Taylor]
Generated description
Georgia Taylor is an English actress best known for her roles in television dramas such as Law & Order: UK and Coronation Street.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Georgia Taylor Target entity description: Georgia Taylor is an English actress best known for her roles in television dramas such as Law & Order: UK and Coronation Street.
-
A.
Mary Elizabeth Taylor
Mary Elizabeth Taylor was a daughter of U.S. President Zachary Taylor and a member of the prominent Taylor family in early 19th-century America.
-
B.
Adelaide Taylor
Adelaide Taylor is the daughter of Australian actress and director Rachel Griffiths.
-
C.
Harlem Taylor
Harlem Taylor is the son of American rapper and West Coast hip hop artist The Game.
-
D.
Georgia Davis
Georgia Davis was the second wife of American comedian and actor Red Skelton, with whom she shared both personal and professional aspects of his entertainment career.
-
E.
Georgia Thomas
Georgia Thomas is a fictional attorney and partner at the Boston law firm in the television series "Ally McBeal."
- 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_69da626a67648190af9653832a3aeced |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e65d13a8a88190bf5f4f697793f4c9 |
completed | April 20, 2026, 5:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0804fb67ac819083a9dd9c11651acd |
completed | May 16, 2026, 5:47 a.m. |
| NEDg | Description generation | batch_6a0806fe43148190820bc3246c6697db |
completed | May 16, 2026, 5:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a08075f1a48819082f217db39841fdb |
completed | May 16, 2026, 5:57 a.m. |
Created at: April 11, 2026, 3:28 p.m.