Triple
T4817000
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Praskovya Fyodorovna |
E107611
|
entity |
| Predicate | treatsIvanIlyichAs |
P44049
|
FINISHED |
| Object | burden |
—
|
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: burden | Statement: [Praskovya Fyodorovna, treatsIvanIlyichAs, burden]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: treatsIvanIlyichAs Context triple: [Praskovya Fyodorovna, treatsIvanIlyichAs, burden]
-
A.
treats
Indicates that one entity provides medical care or therapeutic intervention to another entity.
-
B.
treatsCharacter
chosen
Indicates how one character behaves toward or interacts with another character, especially in terms of care, respect, or mistreatment.
-
C.
regardsSufferingAs
Indicates how an entity perceives, evaluates, or emotionally responds to the suffering of another entity.
-
D.
hasHumorousTreatmentOf
Indicates that one entity presents or portrays another entity in a humorous, comedic, or joking manner.
-
E.
treatment
Indicates that one entity is used as a medical or therapeutic intervention to address, manage, or cure a condition affecting another entity.
- F. None of above.
Provenance (3 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_69bd43f9efa081908314cb3e94fa1695 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6ddd17d881909f7731ff2b460e83 |
completed | March 20, 2026, 3:55 p.m. |
| PD | Predicate disambiguation | batch_69bd6c1dfa3481909d240d50ed0ee38c |
completed | March 20, 2026, 3:47 p.m. |
Created at: March 20, 2026, 1:24 p.m.