Triple
T16585170
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | James Frain |
E402937
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Elizabeth
"Elizabeth" is a 1998 historical drama film about the early reign of Queen Elizabeth I of England, acclaimed for its performances and direction.
|
E64313
|
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: [James Frain, notableWork, Elizabeth]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elizabeth Context triple: [James Frain, notableWork, Elizabeth]
-
A.
Elizabeth
Elizabeth is the middle name of Diane Elizabeth Dern, an individual likely known in relation to the Dern family.
-
B.
Elizabeth
Elizabeth is the birth name of American actress and singer Betty Hutton, a popular Hollywood star of the 1940s and 1950s.
-
C.
Elizabeth
Elizabeth is the given first name of American actress Bess Armstrong, known for her work in film and television since the late 1970s.
-
D.
Elizabeth
Elizabeth is the middle name of Tipper Gore, the American social issues advocate and former Second Lady of the United States.
-
E.
Elizabeth
Elizabeth was the birth name of Princess Elizabeth of England, who later became Queen Elizabeth I, the influential Tudor monarch of England.
- 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: [James Frain, notableWork, Elizabeth]
Generated description
"Elizabeth" is a 1998 historical drama film about the early reign of Queen Elizabeth I of England, acclaimed for its performances and direction.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Elizabeth Target entity description: "Elizabeth" is a 1998 historical drama film about the early reign of Queen Elizabeth I of England, acclaimed for its performances and direction.
-
A.
Elizabeth
chosen
"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 first name of acclaimed New Zealand filmmaker Jane Campion, known for directing films such as "The Piano."
-
C.
Elizabeth
Elizabeth was the birth name of Princess Elizabeth of England, who later became Queen Elizabeth I, the influential Tudor monarch of England.
-
D.
Elizabeth
"Elizabeth" is a French film featuring actress Fanny Ardant in a prominent role.
-
E.
Elizabeth
Elizabeth is a supporting character in the crime drama film "Hustlers," which follows a group of strippers who scheme to swindle wealthy Wall Street clients.
- F. None of above.
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_69d88387363c8190a97a0c942130de97 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3599b057881909fcb8bbb156633a8 |
completed | April 18, 2026, 10:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a006ee8812c81908ef74636bf39d44a |
completed | May 10, 2026, 11:41 a.m. |
| NEDg | Description generation | batch_6a0070024cb4819092ee0ce1320f0905 |
completed | May 10, 2026, 11:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00707959a081909fc04947624abbe5 |
completed | May 10, 2026, 11:48 a.m. |
Created at: April 10, 2026, 5:16 a.m.