Triple
T10523873
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr Bennet |
E248244
|
entity |
| Predicate | relationshipWithLydia |
P94373
|
FINISHED |
| Object | Indulgent but careless |
—
|
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: Indulgent but careless | Statement: [Mr Bennet, relationshipWithLydia, Indulgent but careless]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipWithLydia Context triple: [Mr Bennet, relationshipWithLydia, Indulgent but careless]
-
A.
relationshipToLaurie
Indicates the specific type of relationship or connection that an entity has to Laurie.
-
B.
relationshipToHannah
Indicates the specific type of relationship or connection that an entity has to Hannah.
-
C.
relationshipTypeWithLily Owens
Indicates the specific nature or category of relational connection that an entity has with Lily Owens.
-
D.
relationshipToSophie
Indicates the specific type of personal or social connection that an entity has to Sophie.
-
E.
relationshipToCatherine
Indicates the specific familial, social, or interpersonal connection that one entity has to the person named Catherine.
- 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_69d381c5c7448190bec34bee7ec72bac |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d509e155b08190996325bf484ec55d |
completed | April 7, 2026, 1:42 p.m. |
| PD | Predicate disambiguation | batch_69d4fb94fa10819091f585bab4379c6f |
completed | April 7, 2026, 12:41 p.m. |
| PDg | Predicate description generation | batch_69d4fe06d4a48190b1a45dd1d4e16df0 |
completed | April 7, 2026, 12:52 p.m. |
Created at: April 6, 2026, 12:29 p.m.