Triple
T25704508
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lady Booby |
E644549
|
entity |
| Predicate | relationshipTypeWith Joseph Andrews |
P192746
|
FINISHED |
| Object | employer–servant |
—
|
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: employer–servant | Statement: [Lady Booby, relationshipTypeWith Joseph Andrews, employer–servant]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWith Joseph Andrews Context triple: [Lady Booby, relationshipTypeWith Joseph Andrews, employer–servant]
-
A.
relationshipTypeWithTomJones
Indicates the specific type or nature of the relationship that an entity has with Tom Jones.
-
B.
relationshipToFalstaff
Indicates the specific interpersonal or social connection that one entity has to Falstaff.
-
C.
relationshipTypeWithJakeBarnes
Indicates the specific nature or category of relationship that an entity has with Jake Barnes.
-
D.
relationshipTypeWithMargaretSchlegel
Indicates the specific type or nature of the relationship that an entity has with Margaret Schlegel.
-
E.
relationshipTypeWithWilhelm Meister
Indicates the specific type or nature of the relationship that an entity has with Wilhelm Meister.
- 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_69e77e83c8ec8190bf52fcdac4838984 |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69fd2a215d6c8190a1a428ccaee603f1 |
completed | May 8, 2026, 12:11 a.m. |
| PD | Predicate disambiguation | batch_69fd28ef19688190bb8370f2812a43e7 |
completed | May 8, 2026, 12:06 a.m. |
| PDg | Predicate description generation | batch_69fd2a2095f88190bfcbcb2973516ffc |
completed | May 8, 2026, 12:11 a.m. |
Created at: April 21, 2026, 9:01 p.m.