Triple
T7904502
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lieutenant Dan Taylor |
E183537
|
entity |
| Predicate | religiousArc |
P47348
|
FINISHED |
| Object | reconciliation with God |
—
|
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: reconciliation with God | Statement: [Lieutenant Dan Taylor, religiousArc, reconciliation with God]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: religiousArc Context triple: [Lieutenant Dan Taylor, religiousArc, reconciliation with God]
-
A.
religiousTarget
Indicates that an action, policy, or behavior is directed at someone or something specifically because of their religion or religious affiliation.
-
B.
religiousElement
Indicates that something is a component, aspect, or feature associated with a religion or religious practice.
-
C.
religiousFunction
Indicates that one entity serves a religious role, purpose, or function in relation to another entity.
-
D.
religiousTopicAddressed
chosen
Indicates that a subject deals with, discusses, or focuses on a religious theme, issue, or question.
-
E.
subjectReligion
Indicates that the subject is associated with, practices, or adheres to a particular religion.
- 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_69ca828d13088190b222be7aa9f9315c |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3a4331cc8190b50301c78767a850 |
completed | March 31, 2026, 3:06 a.m. |
| PD | Predicate disambiguation | batch_69cae92f9498819085277879e59aa072 |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 5:02 p.m.