Triple
T4040911
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lady Margaret Hall, Oxford |
E83944
|
entity |
| Predicate | religiousFoundation |
P53759
|
FINISHED |
| Object | Anglican |
—
|
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: Anglican | Statement: [Lady Margaret Hall, Oxford, religiousFoundation, Anglican]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: religiousFoundation Context triple: [Lady Margaret Hall, Oxford, religiousFoundation, Anglican]
-
A.
religiousTarget
Indicates that an action, policy, or behavior is directed at someone or something specifically because of their religion or religious affiliation.
-
B.
foundedReligion
Indicates that an entity established or originated a particular religion.
-
C.
religiousAffiliation
Indicates that one entity has a specified religious association, belief system, or denominational membership.
-
D.
religiousElement
Indicates that something is a component, aspect, or feature associated with a religion or religious practice.
-
E.
religiousTopicAddressed
Indicates that a subject deals with, discusses, or focuses on a religious theme, issue, or question.
- 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_69aed92f7cf0819098e0539bdcc3767f |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefb3a9314819095dcf47675eedb48 |
completed | March 9, 2026, 4:54 p.m. |
| PD | Predicate disambiguation | batch_69aef900386481909d04555a9ec9b0e3 |
completed | March 9, 2026, 4:44 p.m. |
| PDg | Predicate description generation | batch_69aef98ed84c81909dc86097df4afd4f |
completed | March 9, 2026, 4:47 p.m. |
Created at: March 9, 2026, 3:37 p.m.