Triple
T35970534
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Strelnikov |
E1040271
|
entity |
| Predicate | relationshipTypeWithYuriZhivago |
P207118
|
FINISHED |
| Object | ideological opponent |
—
|
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: ideological opponent | Statement: [Strelnikov, relationshipTypeWithYuriZhivago, ideological opponent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithYuriZhivago Context triple: [Strelnikov, relationshipTypeWithYuriZhivago, ideological opponent]
-
A.
relationshipTypeWithDmitriGurov
Indicates the specific nature or category of the relationship that an entity has with Dmitri Gurov.
-
B.
hasRelationshipTypeWithNastasyaFilippovna
Indicates that an entity has a specific type of relationship with Nastasya Filippovna.
-
C.
hasRelationshipTypeWith Anastasia Steele
Indicates that an entity has a specific type of relationship or relational role with Anastasia Steele.
-
D.
relationshipTypeWith Konstantin Treplev
Indicates the specific nature or category of relational connection that another entity has with Konstantin Treplev.
-
E.
literaryRelationship
Indicates a relationship between entities that are connected through literature, such as authorship, influence, adaptation, or other text-based associations.
- 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_69f76e27758c81909b711cf38a130aaf |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0895b48190acdd88dc10db7be7 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c82179081908325a59b8539b3a8 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:07 p.m.