Triple
T33738761
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Linda La Hughes |
E864498
|
entity |
| Predicate | hasRelationshipTypeWithTomFarrell |
P206659
|
FINISHED |
| Object | platonic friendship |
—
|
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: platonic friendship | Statement: [Linda La Hughes, hasRelationshipTypeWithTomFarrell, platonic friendship]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRelationshipTypeWithTomFarrell Context triple: [Linda La Hughes, hasRelationshipTypeWithTomFarrell, platonic friendship]
-
A.
hasRelationshipTypeWithJeremyFurlow
Indicates that an entity has a specific, defined type of relationship with Jeremy Furlow.
-
B.
relationshipToTomMarshall
Indicates the nature of a person's relationship or connection to Tom Marshall.
-
C.
hasRelationshipTypeWith Tai Frasier
Indicates that there exists a specific type of relationship between an entity and Tai Frasier.
-
D.
hasRelationshipTypeWith Jim Rockford
Indicates that an entity is connected to Jim Rockford by a specific type of relationship (such as familial, professional, or social).
-
E.
relationshipTypeWithTommy Callahan III
Indicates the specific nature or category of relationship that an entity has with Tommy Callahan III.
- 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_69f3498b24b8819096a65009e521d0e1 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_6a037e0953908190b2930b3c06a40129 |
completed | May 12, 2026, 7:22 p.m. |
| PD | Predicate disambiguation | batch_6a0379f6c3308190b954f7810214ceed |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037e07fe4481909ca21eae7a941ee7 |
completed | May 12, 2026, 7:22 p.m. |
Created at: May 1, 2026, 1:44 a.m.