Triple
T16080136
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Princess Elizabeth of England |
E390086
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Elizabeth
Elizabeth was the birth name of Princess Elizabeth of England, who later became Queen Elizabeth I, the influential Tudor monarch of England.
|
E1193071
|
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: [Princess Elizabeth of England, givenName, Elizabeth]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elizabeth Context triple: [Princess Elizabeth of England, givenName, 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 name of Elizabeth Camilla Julia "Lisl" Godowsky, an individual associated with the Godowsky family.
-
D.
Elizabeth
Elizabeth is the given first name of American actress Bess Armstrong, known for her work in film and television since the late 1970s.
-
E.
Elizabeth
Elizabeth is the middle name of Tipper Gore, the American social issues advocate and former Second Lady of the United States.
- 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: [Princess Elizabeth of England, givenName, Elizabeth]
Generated description
Elizabeth was the birth name of Princess Elizabeth of England, who later became Queen Elizabeth I, the influential Tudor monarch of England.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Elizabeth Target entity description: Elizabeth was the birth name of Princess Elizabeth of England, who later became Queen Elizabeth I, the influential Tudor monarch of England.
-
A.
Elizabeth
Elizabeth was the Duchess of York who later became Queen Elizabeth The Queen Mother, a prominent member of the British royal family in the 20th century.
-
B.
Elizabeth
Elizabeth is the given name of Lady Elizabeth Spencer-Churchill, a member of the prominent Spencer-Churchill aristocratic family in Britain.
-
C.
Elizabeth
Elizabeth is the middle name of Princess Beatrice of York, a member of the British royal family.
-
D.
Elizabeth
Elizabeth is the given name of the renowned Victorian-era English poet Elizabeth Barrett Browning.
-
E.
Elizabeth
Elizabeth Boleyn, Countess of Wiltshire, was an English noblewoman of the early 16th century best known as the mother of Anne Boleyn and grandmother of Queen Elizabeth I.
- 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_69d86daf32ec8190a8c0466c8f49c3c0 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e18448bebc8190b0e84b1da097bf8b |
completed | April 17, 2026, 12:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffe48adec081909623355eabee472c |
completed | May 10, 2026, 1:51 a.m. |
| NEDg | Description generation | batch_69ffe6af1c4081908b57f4dc485fbb14 |
completed | May 10, 2026, 2 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffe769d56081908f723e92d327e315 |
completed | May 10, 2026, 2:03 a.m. |
Created at: April 10, 2026, 4:57 a.m.