Triple
T28203361
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | St Barnabas Church, Dibley |
E716947
|
entity |
| Predicate | hasFictionalClergyMember |
P61558
|
FINISHED |
| Object | Geraldine Granger |
—
|
NE NERFINISHED |
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: Geraldine Granger | Statement: [St Barnabas Church, Dibley, hasFictionalClergyMember, Geraldine Granger]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalClergyMember Context triple: [St Barnabas Church, Dibley, hasFictionalClergyMember, Geraldine Granger]
-
A.
fictionalClergyAssociated
Indicates an association between a fictional clergy member and another entity, such as an institution, group, place, or work, with which they are connected.
-
B.
hasClergyCharacter
Indicates that an entity possesses a religious or clerical role, status, or character.
-
C.
hasFictionalStaffMember
chosen
Indicates that an entity includes or employs a staff member who is a fictional character.
-
D.
hasClergy
Indicates that an organization or institution possesses or is served by members of the clergy.
-
E.
hasNotableClergyman
Indicates that an entity is associated with a clergyman who is distinguished or notable in some recognized way.
- F. None of above.
Provenance (3 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_69efd6b826908190857e6e7dad74ed93 |
completed | April 27, 2026, 9:35 p.m. |
| NER | Named-entity recognition | batch_69fd09840ea88190a2e6d7e577ade717 |
completed | May 7, 2026, 9:52 p.m. |
| PD | Predicate disambiguation | batch_69fd064c49988190afadddbd04d7cb94 |
completed | May 7, 2026, 9:38 p.m. |
Created at: April 27, 2026, 10:33 p.m.