Triple
T28203365
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | St Barnabas Church, Dibley |
E716947
|
entity |
| Predicate | hasFictionalDedication |
P192426
|
FINISHED |
| Object | Saint Barnabas |
—
|
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: Saint Barnabas | Statement: [St Barnabas Church, Dibley, hasFictionalDedication, Saint Barnabas]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalDedication Context triple: [St Barnabas Church, Dibley, hasFictionalDedication, Saint Barnabas]
-
A.
hasFictionalAddressee
Indicates that an entity (such as a text or communication) is directed toward or addressed to an addressee that is fictional rather than a real person or audience.
-
B.
hasFictionalContent
Indicates that something contains or includes material that is imaginary, invented, or not intended to represent real events or facts.
-
C.
hasFictionalAuthor
Indicates that one entity is the fictional or in-universe author of a work attributed to them.
-
D.
hasFictionalWork
Indicates that one entity is the creator, owner, or source of a fictional work associated with another entity.
-
E.
hasFictionComponent
Indicates that something includes, contains, or is composed in part of a fictional element or work.
- 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_69efd6b826908190857e6e7dad74ed93 |
completed | April 27, 2026, 9:35 p.m. |
| NER | Named-entity recognition | batch_69fd0b92f42881908cd77e3f058adcc2 |
completed | May 7, 2026, 10 p.m. |
| PD | Predicate disambiguation | batch_69fd0a3d68d4819094d92040f7c48d7c |
completed | May 7, 2026, 9:55 p.m. |
| PDg | Predicate description generation | batch_69fd0b92150881909b1166fe6d09aa19 |
completed | May 7, 2026, 10 p.m. |
Created at: April 27, 2026, 10:33 p.m.