Triple
T5271843
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sister Louise of Mercy |
E119276
|
entity |
| Predicate | occupationBeforeConvent |
P28984
|
FINISHED |
| Object | royal mistress |
—
|
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: royal mistress | Statement: [Sister Louise of Mercy, occupationBeforeConvent, royal mistress]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: occupationBeforeConvent Context triple: [Sister Louise of Mercy, occupationBeforeConvent, royal mistress]
-
A.
earlierOccupation
chosen
Indicates that one occupation held by an entity occurred before another occupation in that entity’s work history.
-
B.
occupationBeforeEpiscopacy
Indicates the role or occupation a person held prior to becoming a bishop or entering episcopal office.
-
C.
characterFormerOccupation
Indicates that a character previously held a specific occupation but no longer does.
-
D.
tookReligiousVows
Indicates that an entity formally committed to a religious life by taking recognized vows within a religious tradition.
-
E.
forcedIntoMonasticLifeBy
Indicates that one entity compelled another, against their will or under strong pressure, to enter and live a monastic or religious life.
- 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_69bd446c38e081908cdaf113bdf86790 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7d5a23908190a24e79d1b29d6fcf |
completed | March 20, 2026, 5:01 p.m. |
| PD | Predicate disambiguation | batch_69bd77c71268819094f9f5203eed392d |
completed | March 20, 2026, 4:37 p.m. |
Created at: March 20, 2026, 1:51 p.m.