Triple
T13000520
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mary (novel) |
E322156
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object |
Joseph
Joseph is a fictional character in Vladimir Nabokov’s novel "Mary," playing a supporting role in the story’s exploration of memory and lost love.
|
E1015363
|
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: Joseph | Statement: [Mary (novel), hasCharacter, Joseph]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Joseph Context triple: [Mary (novel), hasCharacter, Joseph]
-
A.
Joseph
Joseph is the first name of J. C. R. Licklider, a pioneering computer scientist often regarded as a key figure in the development of the internet and interactive computing.
-
B.
Joseph
Joseph is the full given name of American sportscaster Joe Buck, known for his play-by-play announcing of major NFL and MLB games.
-
C.
Joseph
Joseph is a common masculine given name of Hebrew origin, traditionally interpreted to mean "He will add" or "God increases."
-
D.
Joseph
Joseph is the given name of the renowned British Romantic landscape painter J. M. W. Turner.
-
E.
Joseph
Joseph is the given first name of American voice actor and comedian Joe Alaskey, known for voicing several iconic Looney Tunes characters.
- 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: Joseph Triple: [Mary (novel), hasCharacter, Joseph]
Generated description
Joseph is a fictional character in Vladimir Nabokov’s novel "Mary," playing a supporting role in the story’s exploration of memory and lost love.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Joseph Target entity description: Joseph is a fictional character in Vladimir Nabokov’s novel "Mary," playing a supporting role in the story’s exploration of memory and lost love.
-
A.
Joseph
Joseph is the given name of the iconic comic book character Judge Joseph Dredd, a law enforcement officer in the dystopian future city of Mega-City One.
-
B.
Joseph
Joseph is the husband of Mary in the New Testament and the earthly guardian of Jesus, venerated in Christianity as a model of humility, obedience, and fatherhood.
-
C.
Joseph
Joseph is the given first name of the 19th-century Irish Gothic writer Sheridan Le Fanu.
-
D.
Joseph
Joseph is the given name of Joe DiMaggio, the legendary American baseball center fielder for the New York Yankees.
-
E.
Joseph
Joseph is the middle name of the 19th-century American reformer and Unitarian minister Samuel Joseph May, known for his prominent role in the abolitionist and social reform movements.
- 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_69d807657e8c8190bd9435ee2f823845 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97e9828748190b2ad9ea29180b7d3 |
completed | April 10, 2026, 10:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6c101ea60819098e4d5fb3a9e803a |
completed | May 3, 2026, 3:29 a.m. |
| NEDg | Description generation | batch_69f6c277e6248190870b3bf9869716a7 |
completed | May 3, 2026, 3:35 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6c38bc0b08190b76cb0853d99ad82 |
completed | May 3, 2026, 3:39 a.m. |
Created at: April 9, 2026, 8:46 p.m.