Triple
T13244800
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Empress Anna of Russia |
E315372
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Anna
Anna was an 18th-century Empress of Russia from the Romanov dynasty who ruled the Russian Empire from 1730 to 1740.
|
E133358
|
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: Anna | Statement: [Empress Anna of Russia, givenName, Anna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anna Context triple: [Empress Anna of Russia, givenName, Anna]
-
A.
Anna
Anna is the given name of Anna Murray Douglass, an African American abolitionist and the first wife of Frederick Douglass.
-
B.
Anna
Anna is an actress known for portraying the ambitious and manipulative Lady Macbeth in a production of Shakespeare’s tragedy "Macbeth."
-
C.
Anna
Anna is a biblical figure in the Book of Tobit, known as Tobit's wife and the mother of Tobias.
-
D.
Anna
Anna is a woman whose full name is Mrs. Anna Smith.
-
E.
Anna
Anna of Moscow was a medieval Russian noblewoman and princess associated with the ruling dynasties of Muscovy.
- 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: Anna Triple: [Empress Anna of Russia, givenName, Anna]
Generated description
Anna was an 18th-century Empress of Russia from the Romanov dynasty who ruled the Russian Empire from 1730 to 1740.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Anna Target entity description: Anna was an 18th-century Empress of Russia from the Romanov dynasty who ruled the Russian Empire from 1730 to 1740.
-
A.
Anna
chosen
Anna was Empress of Russia from 1730 to 1740, known for her autocratic rule and the dominance of her German favorites at court.
-
B.
Anna
Anna of Bohemia and Hungary was a 16th-century queen consort of the Romans and later Holy Roman Empress, known for her marriage to Emperor Ferdinand I and her role in uniting the Habsburg and Jagiellonian dynasties.
-
C.
Anna
Anna of Moscow was a medieval Russian noblewoman and princess associated with the ruling dynasties of Muscovy.
-
D.
Anna
Anna is the given name of Anna Laetitia Barbauld, an influential 18th–19th century English poet, essayist, and children's author.
-
E.
Anna
Anna Mikhailovna of Russia was a Russian princess of the Romanov dynasty, known as the daughter of Grand Duke Mikhail Nikolaevich and a member of the imperial family in the late 19th and early 20th centuries.
- 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_69d806b1072881909e46bd212259c5f0 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98d5b06148190a44e698bbe5cd529 |
completed | April 10, 2026, 11:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f72664b9a48190a76e0c3dfaaf3d7a |
completed | May 3, 2026, 10:41 a.m. |
| NEDg | Description generation | batch_69f72a6fdf7c8190a2345e6771228968 |
completed | May 3, 2026, 10:58 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f72ac6b4688190a2052a44b445b14b |
completed | May 3, 2026, 11 a.m. |
Created at: April 9, 2026, 9:23 p.m.