Triple
T14940455
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clarissa Vaughan |
E372510
|
entity |
| Predicate | partner |
P1136
|
FINISHED |
| Object |
Sally
Sally is Clarissa Vaughan’s longtime companion in Michael Cunningham’s novel "The Hours," representing stability and everyday intimacy in Clarissa’s life.
|
E1134258
|
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: Sally | Statement: [Clarissa Vaughan, partner, Sally]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sally Context triple: [Clarissa Vaughan, partner, Sally]
-
A.
Sally
Sally is the Allied reporting name for the Mitsubishi Ki-21, a Japanese twin-engine army bomber used extensively during World War II.
-
B.
Sally
Sally is one of the young children who serve as primary protagonists in Dr. Seuss's classic children's book "The Cat in the Hat."
-
C.
Sally
"Sally" is a comedic stage play by British-American playwright Guy Bolton, known for its lighthearted plot and contribution to early 20th-century musical theatre.
-
D.
Sally
"Sally" is a 1929 American musical film adaptation of the Broadway stage musical, known for its song-and-dance numbers and starring Marilyn Miller in one of her most famous screen roles.
-
E.
Sally
Sally is the given name of the acclaimed American actress and director Sally Field.
- 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: Sally Triple: [Clarissa Vaughan, partner, Sally]
Generated description
Sally is Clarissa Vaughan’s longtime companion in Michael Cunningham’s novel "The Hours," representing stability and everyday intimacy in Clarissa’s life.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sally Target entity description: Sally is Clarissa Vaughan’s longtime companion in Michael Cunningham’s novel "The Hours," representing stability and everyday intimacy in Clarissa’s life.
-
A.
Sally
Sally is the rag-doll heroine of Tim Burton’s animated film "The Nightmare Before Christmas," known for her resourcefulness, independence, and unrequited love for Jack Skellington.
-
B.
Sally
Sally is the given name of English actress Sally Hawkins, known for her acclaimed performances in films such as "Happy-Go-Lucky" and "The Shape of Water."
-
C.
Sally
Sally is one of the young children who serve as primary protagonists in Dr. Seuss's classic children's book "The Cat in the Hat."
-
D.
Sally
Sally is the given name of Sally Shelton-Colby, an American diplomat and former U.S. ambassador.
-
E.
Sally
Sally is the given name of the acclaimed American actress and director Sally Field.
- 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_69d85cc9da0c81908d583ca3f63a3908 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded64a2f24819099b21566756668a2 |
completed | April 15, 2026, 12:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe9dc2ad1c8190a811bb0c20643309 |
completed | May 9, 2026, 2:36 a.m. |
| NEDg | Description generation | batch_69fea1d6b3e8819084177109790a7a33 |
completed | May 9, 2026, 2:54 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fea284f8f4819082567d848b138187 |
completed | May 9, 2026, 2:57 a.m. |
Created at: April 10, 2026, 2:38 a.m.