Triple
T7356754
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Psych |
E169644
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object |
Juliet O'Hara
Juliet O'Hara is a determined and skilled junior detective on the TV series "Psych," known for her sharp instincts, professionalism, and evolving partnership with Shawn Spencer.
|
E658776
|
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: Juliet O'Hara | Statement: [Psych, character, Juliet O'Hara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Juliet O'Hara Context triple: [Psych, character, Juliet O'Hara]
-
A.
Juliet Capulet
Juliet Capulet is the young heroine of William Shakespeare’s tragedy "Romeo and Juliet," renowned as one half of literature’s most famous star-crossed lovers.
-
B.
Juliet Mills
Juliet Mills is a British-American actress known for her work in film, television, and theatre, including acclaimed performances in both comedic and dramatic roles.
-
C.
Juliet Colman
Juliet Colman is the daughter of acclaimed English actor Ronald Colman.
-
D.
Rose Allerton
Rose Allerton was a member of the early 17th-century Allerton family associated with the Pilgrim settlers of Plymouth Colony.
-
E.
Juliette
Juliette is a feminine given name of French origin, widely used in many countries and popularized through literature and film.
- 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: Juliet O'Hara Triple: [Psych, character, Juliet O'Hara]
Generated description
Juliet O'Hara is a determined and skilled junior detective on the TV series "Psych," known for her sharp instincts, professionalism, and evolving partnership with Shawn Spencer.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Juliet O'Hara Target entity description: Juliet O'Hara is a determined and skilled junior detective on the TV series "Psych," known for her sharp instincts, professionalism, and evolving partnership with Shawn Spencer.
-
A.
Juliet Capulet
Juliet Capulet is the young heroine of William Shakespeare’s tragedy "Romeo and Juliet," renowned as one half of literature’s most famous star-crossed lovers.
-
B.
Juliet Mills
Juliet Mills is a British-American actress known for her work in film, television, and theatre, including acclaimed performances in both comedic and dramatic roles.
-
C.
Juliet Colman
Juliet Colman is the daughter of acclaimed English actor Ronald Colman.
-
D.
Rose Allerton
Rose Allerton was a member of the early 17th-century Allerton family associated with the Pilgrim settlers of Plymouth Colony.
-
E.
Juliette
Juliette is a feminine given name of French origin, widely used in many countries and popularized through literature and film.
- 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_69c68a59f2288190877ca15c19b1e822 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f13a62e48190a2d1781a630aa9f0 |
completed | March 27, 2026, 9:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7faa6a5d88190b969b7783edc67b7 |
completed | March 28, 2026, 3:58 p.m. |
| NEDg | Description generation | batch_69c7fc2c90488190bd3aa5bf72606723 |
completed | March 28, 2026, 4:05 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c7fcb5f7f0819081e70f8809bb34ae |
completed | March 28, 2026, 4:07 p.m. |
Created at: March 27, 2026, 3:06 p.m.