Triple
T28604865
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dibley parish church |
E724015
|
entity |
| Predicate | hasFictionalVicar |
P137473
|
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: [Dibley parish church, hasFictionalVicar, Geraldine Granger]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalVicar Context triple: [Dibley parish church, hasFictionalVicar, Geraldine Granger]
-
A.
fictionalClergyAssociated
chosen
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.
fictionalParish
Indicates that an entity is a parish that exists only in fiction rather than in the real world.
-
D.
hasChaplain
Indicates that an entity is served or attended to by a designated chaplain.
-
E.
hasClericalProtagonist
Indicates that the main character in the work is a member of the clergy or holds a religious office.
- 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_69f01d816d7c8190a1fe27e3434041dc |
completed | April 28, 2026, 2:37 a.m. |
| NER | Named-entity recognition | batch_69fd44474ed48190ac372e4c88d762ed |
completed | May 8, 2026, 2:02 a.m. |
| PD | Predicate disambiguation | batch_69fd41ef28a48190a66959be5c964461 |
completed | May 8, 2026, 1:52 a.m. |
Created at: April 28, 2026, 4:26 a.m.