Triple
T11981519
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mistress Page |
E285170
|
entity |
| Predicate | supportsMarriageOf |
P62195
|
FINISHED |
| Object | Anne Page and Fenton |
—
|
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: Anne Page and Fenton | Statement: [Mistress Page, supportsMarriageOf, Anne Page and Fenton]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: supportsMarriageOf Context triple: [Mistress Page, supportsMarriageOf, Anne Page and Fenton]
-
A.
acceptsMarriageTo
Indicates that one entity formally agrees to enter into a marriage with another entity.
-
B.
sameSexMarriage
Indicates that two individuals are married to each other and are of the same sex.
-
C.
hasAttitudeTowardMarriage
chosen
Indicates that an entity holds a particular opinion, feeling, or stance regarding the institution or concept of marriage.
-
D.
marriageType
Indicates the specific legal or social category of a marriage relationship that exists between two spouses.
-
E.
marries
Indicates that one entity enters into a legally or socially recognized marital union with another entity.
- 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_69d6ab44a77c8190a652f4b27164e4ef |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903acbb9081908fe7f8360057785c |
completed | April 10, 2026, 2:05 p.m. |
| PD | Predicate disambiguation | batch_69d902abca70819098291aa51b593708 |
completed | April 10, 2026, 2:01 p.m. |
Created at: April 8, 2026, 9:46 p.m.