Triple
T6046181
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rousanou Monastery |
E134671
|
entity |
| Predicate | numberOfNuns |
P68391
|
FINISHED |
| Object | dozens |
—
|
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: dozens | Statement: [Rousanou Monastery, numberOfNuns, dozens]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfNuns Context triple: [Rousanou Monastery, numberOfNuns, dozens]
-
A.
numberOfSaints
Indicates the numerical count of saints associated with a given entity or context.
-
B.
numberOfMonksApprox
Indicates an approximate count or estimate of how many monks are involved or present in a given context.
-
C.
numberOfParishes
Indicates the total count of parishes associated with a given entity.
-
D.
priestsCalled
Indicates that one or more individuals are given the role, title, or vocation of priest, typically through a formal or divine calling.
-
E.
sisterMonastery
Indicates a formal sister-monastery relationship between two monastic institutions, typically involving mutual support, affiliation, or shared spiritual ties.
- 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_69c00876a69881908088a2626d3b2666 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c056e53f508190864be04bc016c525 |
completed | March 22, 2026, 8:53 p.m. |
| PD | Predicate disambiguation | batch_69c049eb52a08190ac10fd703735f5aa |
completed | March 22, 2026, 7:58 p.m. |
| PDg | Predicate description generation | batch_69c04e8d4a148190bd8f95caae978e1b |
completed | March 22, 2026, 8:18 p.m. |
Created at: March 22, 2026, 4:09 p.m.