Triple
T14942751
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cameron Roberts |
E372571
|
entity |
| Predicate | hasRelationshipTypeWith Vince Tyler |
P116769
|
FINISHED |
| Object | boyfriend |
—
|
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: boyfriend | Statement: [Cameron Roberts, hasRelationshipTypeWith Vince Tyler, boyfriend]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRelationshipTypeWith Vince Tyler Context triple: [Cameron Roberts, hasRelationshipTypeWith Vince Tyler, boyfriend]
-
A.
hasRelationshipTypeWith Tai Frasier
Indicates that there exists a specific type of relationship between an entity and Tai Frasier.
-
B.
relationshipToTinaBordereau
Indicates the specific type of personal or professional relationship an entity has with Tina Bordereau.
-
C.
relationshipTypeWith Eugene Gant
Indicates the specific nature or category of relationship that an entity has with Eugene Gant.
-
D.
hasRelationshipTypeWithCollinFenwick
Indicates that an entity has a specific type of relationship or connection with Collin Fenwick.
-
E.
relationshipToTony
Indicates the specific type of relationship or connection that an entity has with Tony.
- 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_69d85cc9da0c81908d583ca3f63a3908 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded68c1df0819084c0cd61b207d398 |
completed | April 15, 2026, 12:06 a.m. |
| PD | Predicate disambiguation | batch_69de9a588c2c8190b1245a1c406f447c |
completed | April 14, 2026, 7:49 p.m. |
| PDg | Predicate description generation | batch_69deb1a4d8dc8190a4c0841c20f2875f |
completed | April 14, 2026, 9:29 p.m. |
Created at: April 10, 2026, 2:38 a.m.