Triple
T17955233
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anna Pavlovna Scherer |
E448928
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Anna
Anna is the given name of Anna Pavlovna Scherer, a high-society hostess and confidante of the Russian Empress in Leo Tolstoy’s novel "War and Peace."
|
E1301704
|
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: [Anna Pavlovna Scherer, givenName, Anna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anna Context triple: [Anna Pavlovna Scherer, 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 a key female resistance fighter in the World War II adventure film "The Guns of Navarone," whose complex loyalties and actions significantly impact the mission’s outcome.
-
C.
Anna
Anna is a small city in north-central Texas that forms part of the fast-growing suburban region north of Dallas.
-
D.
Anna
Anna is a biblical figure in the Book of Tobit, known as Tobit's wife and the mother of Tobias.
-
E.
Anna
Anna is the given first name of the American actress, comedian, and director Nancy Walker.
- 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: [Anna Pavlovna Scherer, givenName, Anna]
Generated description
Anna is the given name of Anna Pavlovna Scherer, a high-society hostess and confidante of the Russian Empress in Leo Tolstoy’s novel "War and Peace."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Anna Target entity description: Anna is the given name of Anna Pavlovna Scherer, a high-society hostess and confidante of the Russian Empress in Leo Tolstoy’s novel "War and Peace."
-
A.
Anna
Anna is the tragic, aristocratic heroine of Leo Tolstoy’s novel "Anna Karenina," whose passionate affair and struggle against societal norms lead to her downfall.
-
B.
Anna
Anna is the given first name of the fictional character Nana Coupeau from Émile Zola’s novel "Nana."
-
C.
Anna
Anna is the given first name of Eleanor Roosevelt, the influential former First Lady of the United States and human rights advocate.
-
D.
Anna
Anna is the given name of Archduchess Anna Maria Sophia of Austria, a member of the Habsburg royal family.
-
E.
Anna
Anna is the given name of Anna Laetitia Barbauld, an influential 18th–19th century English poet, essayist, and children's author.
- 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_69d8b9f8cca8819099836916c56b7c95 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e4afaf1ddc8190b480147ac35a4912 |
completed | April 19, 2026, 10:34 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a034313e1ec8190a2dc15ed8a4cf992 |
completed | May 12, 2026, 3:11 p.m. |
| NEDg | Description generation | batch_6a0345ad1c8c8190bfabd06fcf247862 |
completed | May 12, 2026, 3:22 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0346008dd881909062ff490df2a721 |
completed | May 12, 2026, 3:23 p.m. |
Created at: April 10, 2026, 10:21 a.m.