Triple
T15442790
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Princess Bibesco |
E369947
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Elizabeth
Elizabeth is the given name of Princess Bibesco, a Romanian-British writer and socialite active in the early 20th century.
|
E1157442
|
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 Bibesco, givenName, Elizabeth]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elizabeth Context triple: [Princess Bibesco, givenName, Elizabeth]
-
A.
Elizabeth
Elizabeth is the formal first name of Bess Truman, who served as First Lady of the United States as the wife of President Harry S. Truman.
-
B.
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.
-
C.
Elizabeth
Elizabeth is the given name of Elizabeth Jane Cochrane, better known as pioneering American investigative journalist Nellie Bly.
-
D.
Elizabeth
Elizabeth of Denmark was a 16th-century Danish princess who became Electress of Brandenburg through her marriage to Joachim II Hector.
-
E.
Elizabeth
Elizabeth is the given first name of American actress Bess Armstrong, known for her work in film and television since the late 1970s.
- 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 Bibesco, givenName, Elizabeth]
Generated description
Elizabeth is the given name of Princess Bibesco, a Romanian-British writer and socialite active in the early 20th century.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Elizabeth Target entity description: Elizabeth is the given name of Princess Bibesco, a Romanian-British writer and socialite active in the early 20th century.
-
A.
Elizabeth
Elizabeth is the given name of Princess Elizabeth of Yugoslavia, a Yugoslav royal and public figure.
-
B.
Elizabeth
Elizabeth is the middle name of Princess Beatrice of York, a member of the British royal family.
-
C.
Elizabeth
Elizabeth was a Greek and Danish princess of the early 20th century, born into the royal families of both Greece and Denmark.
-
D.
Elizabeth
Elizabeth is the given name of the renowned Victorian-era English poet Elizabeth Barrett Browning.
-
E.
Elizabeth
Elizabeth is the given name of Lady Elizabeth Spencer-Churchill, a member of the prominent Spencer-Churchill aristocratic family in Britain.
- 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_69d85a19180081909925012fbf4e62a3 |
completed | April 10, 2026, 2:02 a.m. |
| NER | Named-entity recognition | batch_69e03ef55f5c8190a32b1b6ad1daf454 |
completed | April 16, 2026, 1:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff21a3d1f481908f6795656514b2b4 |
completed | May 9, 2026, 11:59 a.m. |
| NEDg | Description generation | batch_69ff2299ac9481909ad213ff5bb01db3 |
completed | May 9, 2026, 12:03 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff2325e6a48190bee256ef8720ba8e |
completed | May 9, 2026, 12:05 p.m. |
Created at: April 10, 2026, 3:21 a.m.