Triple
T14880284
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cass Silenski |
E349981
|
entity |
| Predicate | relationshipTypeWithRufusScott |
P104437
|
FINISHED |
| Object | acquaintance |
—
|
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: acquaintance | Statement: [Cass Silenski, relationshipTypeWithRufusScott, acquaintance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithRufusScott Context triple: [Cass Silenski, relationshipTypeWithRufusScott, acquaintance]
-
A.
relationshipType
Indicates the specific kind of relationship that exists between two or more entities.
-
B.
relationshipTypeWithRobertCohn
chosen
Indicates the specific nature or category of relationship that an entity has with Robert Cohn.
-
C.
opusRelationship
Indicates a relationship between creative works (opuses), such as versions, adaptations, or parts within a larger compositional whole.
-
D.
relationshipToCharacter
Indicates the specific type of personal, social, or narrative connection that one entity has to a given character.
-
E.
relationshipCharacterizedAs
Indicates that one relationship is described, defined, or typified in terms of another specified characteristic or relational type.
- 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_69d822ee4f408190b6ac3b2fa434f0df |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded5e622388190b2bf91cd10b9821d |
completed | April 15, 2026, 12:03 a.m. |
| PD | Predicate disambiguation | batch_69de8c1a2bcc81908f914e2e2ced65eb |
completed | April 14, 2026, 6:48 p.m. |
Created at: April 10, 2026, 1:55 a.m.