Triple
T36596902
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Good Women |
E902826
|
entity |
| Predicate | equivalentClass |
P6530
|
FINISHED |
| Object | Perfectae |
—
|
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: Perfectae | Statement: [Good Women, equivalentClass, Perfectae]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: equivalentClass Context triple: [Good Women, equivalentClass, Perfectae]
-
A.
hasEquivalenceClass
Indicates that something belongs to, or is associated with, a group of entities that are considered equivalent under a specified relation.
-
B.
equivalentTo
chosen
Indicates that two entities represent the same concept, value, or state, and can be treated as interchangeable in the given context.
-
C.
equivalentIn
Indicates that two entities are considered logically or functionally the same in meaning, status, or effect within a given context.
-
D.
equivalentGrade
Indicates that two grades are considered equal in value or standing within a given grading system.
-
E.
hasEquivalent
Indicates that two entities are considered equal in value, meaning, or function within a given context.
- 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_69f76e66b7b88190848f7a3e1188915f |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7c371931c8190afb1d4dd5157f92c |
completed | May 3, 2026, 9:51 p.m. |
| PD | Predicate disambiguation | batch_69f7c1baf25c8190a78dd54a400d2c50 |
completed | May 3, 2026, 9:44 p.m. |
Created at: May 3, 2026, 4:11 p.m.