Triple
T29143112
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Samuel Norton |
E738690
|
entity |
| Predicate | usesReligiousRhetoric |
P170157
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Samuel Norton, usesReligiousRhetoric, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesReligiousRhetoric Context triple: [Samuel Norton, usesReligiousRhetoric, yes]
-
A.
hasReligious
Indicates that an entity is associated with, practices, or adheres to a particular religion or religious affiliation.
-
B.
usedForReligiousLanguage
Indicates that something is employed specifically in the context of religious language, such as for expressing, communicating, or performing religious beliefs, practices, or rituals.
-
C.
religiousArgumentType
Indicates that the relationship involves classifying or characterizing an argument according to its religious nature or type.
-
D.
positionOnReligiousLanguage
Indicates a stance or viewpoint someone holds regarding how religious language should be understood, interpreted, or used.
-
E.
usedReligionFor
Indicates that an entity employed religion as a means or tool to achieve some purpose, goal, or effect.
- 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_69f07cb3adb48190a9e0e169cd026634 |
completed | April 28, 2026, 9:24 a.m. |
| NER | Named-entity recognition | batch_69f68b121eac81909e90416207bc1157 |
completed | May 2, 2026, 11:38 p.m. |
| PD | Predicate disambiguation | batch_69f6860def1c81909d79e1f088c4b5e5 |
completed | May 2, 2026, 11:17 p.m. |
| PDg | Predicate description generation | batch_69f68a160374819084d720985f800dfc |
completed | May 2, 2026, 11:34 p.m. |
Created at: April 28, 2026, 11:38 a.m.