Triple
T9404233
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pimen |
E226543
|
entity |
| Predicate | relationshipToGrigoryOtrepiev |
P88988
|
FINISHED |
| Object | spiritual mentor |
—
|
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: spiritual mentor | Statement: [Pimen, relationshipToGrigoryOtrepiev, spiritual mentor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToGrigoryOtrepiev Context triple: [Pimen, relationshipToGrigoryOtrepiev, spiritual mentor]
-
A.
relationshipToPavelVlasov
Indicates the nature or type of relationship an entity has with Pavel Vlasov.
-
B.
relationshipToPierreBezukhov
Indicates the specific type of personal or social relationship an entity has to Pierre Bezukhov.
-
C.
relationshipWithMarinaMniszech
Indicates that an entity has a personal, political, or social relationship with Marina Mniszech.
-
D.
PierreBezukhovPortrayedBy
Indicates that a particular actor portrays the character Pierre Bezukhov in a performance or adaptation.
-
E.
relationshipToMoscow
Indicates the type or nature of a connection, association, or relevance that something has specifically to Moscow.
- 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_69ca843170f88190800a8ab2b5fc568e |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd51c125dc8190a6438cf0ee23e7a9 |
completed | April 1, 2026, 5:11 p.m. |
| PD | Predicate disambiguation | batch_69cca54c37f88190bddccf28e5fe5c84 |
completed | April 1, 2026, 4:55 a.m. |
| PDg | Predicate description generation | batch_69cca9d07eb08190866eb15333386dfa |
completed | April 1, 2026, 5:14 a.m. |
Created at: March 30, 2026, 7:46 p.m.