Triple
T3059243
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Museum of the History of Religion and Atheism |
E60554
|
entity |
| Predicate | portrayedClergyAs |
P28250
|
FINISHED |
| Object | agents of reaction |
—
|
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: agents of reaction | Statement: [Museum of the History of Religion and Atheism, portrayedClergyAs, agents of reaction]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portrayedClergyAs Context triple: [Museum of the History of Religion and Atheism, portrayedClergyAs, agents of reaction]
-
A.
portraysReligionAs
chosen
Indicates that one entity represents, depicts, or characterizes a religion in a particular way.
-
B.
clergyCan
Indicates that members of the clergy are permitted or authorized to perform a specified action or exercise a particular role or function.
-
C.
clergyView
Indicates that a member of the clergy holds a particular opinion, perspective, or evaluative stance toward something.
-
D.
hasClergy
Indicates that an organization or institution possesses or is served by members of the clergy.
-
E.
workedAsClergymanIn
Indicates that a person served in a religious or clerical role within a specified place or institution.
- 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_69ad8578137c81908259dcb27c7d6d7c |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ad9e1741648190b710b7022252498d |
completed | March 8, 2026, 4:04 p.m. |
| PD | Predicate disambiguation | batch_69ad962326e081909d5521c3d3ea3158 |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 3:02 p.m.