Triple
T5254044
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Professor Burris |
E118655
|
entity |
| Predicate | relationshipToFrazier |
P38921
|
FINISHED |
| Object | college 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: college acquaintance | Statement: [Professor Burris, relationshipToFrazier, college acquaintance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToFrazier Context triple: [Professor Burris, relationshipToFrazier, college acquaintance]
-
A.
relationshipToCharacter
chosen
Indicates the specific type of personal, social, or narrative connection that one entity has to a given character.
-
B.
inRelationshipWith
Indicates that two entities are mutually involved in a defined personal, romantic, or partnership relationship with each other.
-
C.
relationshipType
Indicates the specific kind of relationship that exists between two or more entities.
-
D.
relationshipPlannedWith
Indicates that a relationship between two entities has been intentionally arranged or scheduled to occur in the future.
-
E.
termRelationTo
Indicates a general relational association between one term and another, without specifying the exact nature of that relationship.
- 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_69bd446978108190bb5f9c5c23d93f88 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7ba1cca88190bebd516851b9bf7f |
completed | March 20, 2026, 4:53 p.m. |
| PD | Predicate disambiguation | batch_69bd77c30bac8190a883ca45da35d667 |
completed | March 20, 2026, 4:37 p.m. |
Created at: March 20, 2026, 1:50 p.m.