Triple
T27596235
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Val Toriello |
E699904
|
entity |
| Predicate | hasRelationshipTypeWith Fran Fine |
P198306
|
FINISHED |
| Object | best friend |
—
|
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: best friend | Statement: [Val Toriello, hasRelationshipTypeWith Fran Fine, best friend]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRelationshipTypeWith Fran Fine Context triple: [Val Toriello, hasRelationshipTypeWith Fran Fine, best friend]
-
A.
hasRelationshipTypeWith Frank Drebin
Indicates that there exists a specific type of relationship between an entity and Frank Drebin.
-
B.
hasRelationshipTypeWith Tai Frasier
Indicates that there exists a specific type of relationship between an entity and Tai Frasier.
-
C.
relationshipTypeWith Francesca Johnson
Indicates the specific nature or category of the relationship that an entity has with Francesca Johnson.
-
D.
hasRelationshipTypeWith Alexandra Bergson
Indicates that there exists a specific type or category of relationship between an entity and Alexandra Bergson.
-
E.
hasRelationshipTypeWith Vince Tyler
Indicates that an entity is connected to Vince Tyler by a specific, characterized type of relationship.
- 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_69ef6a4d71f081909a1235763206b691 |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69fedd5a5f4c8190acce88db56303703 |
completed | May 9, 2026, 7:08 a.m. |
| PD | Predicate disambiguation | batch_69fed910b31c8190ae837163d146738d |
completed | May 9, 2026, 6:49 a.m. |
| PDg | Predicate description generation | batch_69fedd5915588190bd884054e4be0414 |
completed | May 9, 2026, 7:08 a.m. |
Created at: April 27, 2026, 2:06 p.m.