Triple
T13606888
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fanny Ardant |
E325084
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Elizabeth
"Elizabeth" is a French film featuring actress Fanny Ardant in a prominent role.
|
E1050533
|
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: Elizabeth | Statement: [Fanny Ardant, notableWork, Elizabeth]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elizabeth Context triple: [Fanny Ardant, notableWork, Elizabeth]
-
A.
Elizabeth
Elizabeth "Betty" Ford was the influential First Lady of the United States from 1974 to 1977, renowned for her advocacy on women's rights, breast cancer awareness, and addiction treatment.
-
B.
Elizabeth
Elizabeth is the middle name of Diane Elizabeth Dern, an individual likely known in relation to the Dern family.
-
C.
Elizabeth
Elizabeth is an alternate given name associated with Mary Surratt, the American boardinghouse owner convicted and executed for her role in the conspiracy to assassinate President Abraham Lincoln.
-
D.
Elizabeth
Elizabeth is the middle name of Princess Beatrice of York, a member of the British royal family.
-
E.
Elizabeth
Elizabeth is the full given name of Betsy McCaughey, an American politician, writer, and former lieutenant governor of New York.
- 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: Elizabeth Triple: [Fanny Ardant, notableWork, Elizabeth]
Generated description
"Elizabeth" is a French film featuring actress Fanny Ardant in a prominent role.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Elizabeth Target entity description: "Elizabeth" is a French film featuring actress Fanny Ardant in a prominent role.
-
A.
Elizabeth
"Elizabeth" is a 1998 historical drama film that chronicles the early reign of Queen Elizabeth I of England, starring Cate Blanchett in the title role.
-
B.
Elizabeth
Elizabeth is the birth name of American actress Téa Leoni, known for her roles in film and television such as "Madam Secretary."
-
C.
Elizabeth
Elizabeth is the first name of acclaimed New Zealand filmmaker Jane Campion, known for directing films such as "The Piano."
-
D.
Elizabeth
Elizabeth is a comedic, high-strung fiancée character in the 1974 Mel Brooks film "Young Frankenstein," known for her dramatic personality and memorable scenes.
-
E.
Elizabeth
Elizabeth is the given name of filmmaker Elizabeth Chai Vasarhelyi, known for her acclaimed documentary work including the Oscar-winning film "Free Solo."
- 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_69d80769eaf081909d82f44e484d6113 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb07e442c819086a8cbb967c03ad3 |
completed | April 12, 2026, 2:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f77f7fcab0819091146d54d56f08d7 |
completed | May 3, 2026, 5:01 p.m. |
| NEDg | Description generation | batch_69f78125632881908d601ee4c4aaae35 |
completed | May 3, 2026, 5:08 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f781e32cb48190abc83e65405ac8ac |
completed | May 3, 2026, 5:12 p.m. |
Created at: April 9, 2026, 9:50 p.m.