Triple
T17610557
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Life of Macrina |
E428953
|
entity |
| Predicate | portraysMacrinaAs |
P100368
|
FINISHED |
| Object | founder of a women’s monastic community |
—
|
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: founder of a women’s monastic community | Statement: [Life of Macrina, portraysMacrinaAs, founder of a women’s monastic community]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portraysMacrinaAs Context triple: [Life of Macrina, portraysMacrinaAs, founder of a women’s monastic community]
-
A.
portraysMacintoshAs
Indicates that a subject represents or depicts the Macintosh in a particular way, role, or characterization.
-
B.
portraysPersonAs
chosen
Indicates that one entity represents, depicts, or characterizes another person in a particular way or role.
-
C.
portraysFictionalEntity
Indicates that one entity depicts, represents, or plays the role of a fictional character or figure.
-
D.
portraysActorAs
Indicates that one entity depicts or represents an actor in a particular role, character, or manner.
-
E.
portraysSnakeAs
Indicates that one entity represents or depicts a snake in a particular way, often emphasizing certain traits, roles, or symbolic meanings.
- 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_69d889e1c6148190ba76241e74688f8b |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e46d2d294881908380b2ab0b4d2503 |
completed | April 19, 2026, 5:50 a.m. |
| PD | Predicate disambiguation | batch_69e3cdd7da34819099bc9481c5a79bab |
completed | April 18, 2026, 6:30 p.m. |
Created at: April 10, 2026, 5:51 a.m.