Triple
T5849322
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Count Kirill Vladimirovich Bezukhov |
E129988
|
entity |
| Predicate | relationshipToPierreBezukhov |
P66706
|
FINISHED |
| Object | illegitimate father |
—
|
LITERAL FINISHED |
How this triple was built (2 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: illegitimate father | Statement: [Count Kirill Vladimirovich Bezukhov, relationshipToPierreBezukhov, illegitimate father]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToPierreBezukhov Context triple: [Count Kirill Vladimirovich Bezukhov, relationshipToPierreBezukhov, illegitimate father]
-
A.
relationshipToCatherine
Indicates the specific familial, social, or interpersonal connection that one entity has to the person named Catherine.
-
B.
relationshipToSophie
Indicates the specific type of personal or social connection that an entity has to Sophie.
-
C.
succeededByAsHusbandOfEudoxia
Indicates that one person became the subsequent husband of Eudoxia, following a previous husband in that role.
-
D.
relationshipWithMarinaMniszech
Indicates that an entity has a personal, political, or social relationship with Marina Mniszech.
-
E.
relationshipToAuntEller
Indicates the specific familial relationship that an entity has to Aunt Eller (e.g., whether and how they are related to her).
- F. None of above. chosen
Provenance (4 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_69c0084de39081909eb34e6bed74215a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03c9239e08190bff7ef2bd6d21ae0 |
completed | March 22, 2026, 7:01 p.m. |
| PD | Predicate disambiguation | batch_69c0334412388190bc594794ec5754f9 |
completed | March 22, 2026, 6:21 p.m. |
| PDg | Predicate description generation | batch_69c03c8d579081909d7b97fc9014b5d7 |
completed | March 22, 2026, 7:01 p.m. |
Created at: March 22, 2026, 3:55 p.m.