Triple
T9801700
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | McKaley Miller |
E237851
|
entity |
| Predicate | portrayed |
P1668
|
FINISHED |
| Object |
Sophia Doyle
Sophia Doyle is a fictional character played by American actress McKaley Miller, known from her work in film and television.
|
E824642
|
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: Sophia Doyle | Statement: [McKaley Miller, portrayed, Sophia Doyle]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sophia Doyle Context triple: [McKaley Miller, portrayed, Sophia Doyle]
-
A.
Sophia Hitchens
Sophia Hitchens is the daughter of the late British-American author and polemicist Christopher Hitchens.
-
B.
Emma Doyle
Emma Doyle is an American political aide who has served in senior White House operational and advisory roles, including as a top staffer in the Trump administration.
-
C.
Grace Delaney
Grace Delaney is a central character in the novel "Running Dog," known for her involvement in the dark, conspiratorial world surrounding a mysterious and controversial film.
-
D.
Sophia Pitt
Sophia Pitt was an 18th-century English gentlewoman known primarily as the wife of Royal Navy admiral Sir George Pocock.
-
E.
Mia Dolan
Mia Dolan is an aspiring actress in Los Angeles and one of the two central protagonists of the musical film "La La Land."
- 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: Sophia Doyle Triple: [McKaley Miller, portrayed, Sophia Doyle]
Generated description
Sophia Doyle is a fictional character played by American actress McKaley Miller, known from her work in film and television.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sophia Doyle Target entity description: Sophia Doyle is a fictional character played by American actress McKaley Miller, known from her work in film and television.
-
A.
Sophia Hitchens
Sophia Hitchens is the daughter of the late British-American author and polemicist Christopher Hitchens.
-
B.
Emma Doyle
Emma Doyle is an American political aide who has served in senior White House operational and advisory roles, including as a top staffer in the Trump administration.
-
C.
Grace Delaney
Grace Delaney is a central character in the novel "Running Dog," known for her involvement in the dark, conspiratorial world surrounding a mysterious and controversial film.
-
D.
Sophia Pitt
Sophia Pitt was an 18th-century English gentlewoman known primarily as the wife of Royal Navy admiral Sir George Pocock.
-
E.
Mia Dolan
Mia Dolan is an aspiring actress in Los Angeles and one of the two central protagonists of the musical film "La La Land."
- 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_69ca84dd4608819097ff4ed00feca280 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda62b41048190bcef70a7591830c6 |
completed | April 1, 2026, 11:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1d5aecdec81909fae349945406c6c |
completed | April 5, 2026, 3:23 a.m. |
| NEDg | Description generation | batch_69d1d6463fe481908e5d3f3fd22cffa4 |
completed | April 5, 2026, 3:25 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1d6dc0bd8819082a5ad417ca87a76 |
completed | April 5, 2026, 3:28 a.m. |
Created at: March 30, 2026, 8:29 p.m.