Triple
T20393346
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elizabeth Richeza of Poland |
E500137
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Elizabeth
Elizabeth was the given name of Elizabeth Richeza of Poland, a medieval Polish princess and queen consort in Central Europe.
|
E1428418
|
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: [Elizabeth Richeza of Poland, givenName, Elizabeth]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elizabeth Context triple: [Elizabeth Richeza of Poland, givenName, Elizabeth]
-
A.
Elizabeth
Elizabeth is the given first name of American actress Bess Armstrong, known for her work in film and television since the late 1970s.
-
B.
Elizabeth
Elizabeth is the middle name of Tipper Gore, the American social issues advocate and former Second Lady of the United States.
-
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 the birth name of American actress Beanie Feldstein, known for her roles in films like "Booksmart" and "Lady Bird."
-
E.
Elizabeth
Elizabeth is a fictional character in John Steinbeck’s novel "To a God Unknown," playing a key role in the protagonist’s family and the story’s exploration of faith, land, and sacrifice.
- 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: [Elizabeth Richeza of Poland, givenName, Elizabeth]
Generated description
Elizabeth was the given name of Elizabeth Richeza of Poland, a medieval Polish princess and queen consort in Central Europe.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Elizabeth Target entity description: Elizabeth was the given name of Elizabeth Richeza of Poland, a medieval Polish princess and queen consort in Central Europe.
-
A.
Elizabeth
Elizabeth was a medieval noblewoman who held the title of Duchess of Bavaria and was known as Elizabeth of Hungary.
-
B.
Elizabeth
Elizabeth was the birth name of Princess Elizabeth of England, who later became Queen Elizabeth I, the influential Tudor monarch of England.
-
C.
Elizabeth
Elizabeth is the given name of Princess Elizabeth of Yugoslavia, a Yugoslav royal and public figure.
-
D.
Elizabeth
Elizabeth was a medieval English noblewoman, the daughter of John of Gaunt and granddaughter of King Edward III.
-
E.
Elizabeth
Elizabeth is the given name of Princess Bibesco, a Romanian-British writer and socialite active in the early 20th century.
- 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_69e0b4a71ebc8190b153a36c738730f4 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6791144788190a0ab42cf0141b6a3 |
completed | April 20, 2026, 7:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0876260e548190b0ca1cdee28fe72d |
completed | May 16, 2026, 1:50 p.m. |
| NEDg | Description generation | batch_6a0877d6b364819080b5e701acea02aa |
completed | May 16, 2026, 1:57 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a08787cfe6c8190881554ead3cfd248 |
completed | May 16, 2026, 2 p.m. |
Created at: April 16, 2026, 11:28 a.m.