Triple
T34034717
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ginevra Fanshawe |
E872758
|
entity |
| Predicate | relationshipTypeWith Dr. John Graham Bretton |
P205289
|
FINISHED |
| Object | love interest |
—
|
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: love interest | Statement: [Ginevra Fanshawe, relationshipTypeWith Dr. John Graham Bretton, love interest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWith Dr. John Graham Bretton Context triple: [Ginevra Fanshawe, relationshipTypeWith Dr. John Graham Bretton, love interest]
-
A.
relationshipTypeWithJohnMurdoch
Indicates the specific nature or category of the relationship that an entity has with John Murdoch.
-
B.
relationshipWithJohnBennett
Indicates that there exists some specified type of relationship or association between an entity and John Bennett.
-
C.
relationshipTypeWithFredGraham
Indicates the specific nature or category of relationship that an entity has with Fred Graham.
-
D.
relationshipTypeWithJohnLuther
Indicates the specific nature or category of the relationship an entity has with John Luther.
-
E.
relationshipTypeWith Alicia Johns
Indicates the specific type or nature of the relationship that an entity has with Alicia Johns.
- 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_69f349a2527c81909a7cd4bda94d70ad |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379f963908190846d232f386fd98f |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c80ba448190853011097a151b7e |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 1, 2026, 1:51 a.m.