Triple
T22370264
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boris Trigorin |
E553019
|
entity |
| Predicate | relationshipTypeWith Konstantin Treplev |
P147400
|
FINISHED |
| Object | rivalry |
—
|
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: rivalry | Statement: [Boris Trigorin, relationshipTypeWith Konstantin Treplev, rivalry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWith Konstantin Treplev Context triple: [Boris Trigorin, relationshipTypeWith Konstantin Treplev, rivalry]
-
A.
relationshipToPolinaAlexandrovna
Indicates the specific type of personal or social relationship that one entity has with Polina Alexandrovna.
-
B.
relationshipToOnegin
Indicates the specific interpersonal or familial relationship that one entity has to the person named Onegin.
-
C.
relationshipToGustav von Aschenbach
Indicates the specific type of personal, social, or emotional connection an entity has to Gustav von Aschenbach.
-
D.
relationshipToPavelVlasov
Indicates the nature or type of relationship an entity has with Pavel Vlasov.
-
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_69e11e4c03248190a26a5060ea6973ee |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f158032b748190ad36c7e3809304e9 |
completed | April 29, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69e73011e6388190a05edf137f488441 |
completed | April 21, 2026, 8:06 a.m. |
| PDg | Predicate description generation | batch_69e7342e9a0081909257210a81c96b29 |
completed | April 21, 2026, 8:24 a.m. |
Created at: April 16, 2026, 8:44 p.m.