Triple
T4361609
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Milbanke family |
E98674
|
entity |
| Predicate | hasNotableMarriage |
P55778
|
FINISHED |
| Object | marriage of Annabella Milbanke to Lord Byron |
—
|
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: marriage of Annabella Milbanke to Lord Byron | Statement: [Milbanke family, hasNotableMarriage, marriage of Annabella Milbanke to Lord Byron]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableMarriage Context triple: [Milbanke family, hasNotableMarriage, marriage of Annabella Milbanke to Lord Byron]
-
A.
hasMarriage
Indicates a marital relationship exists between the two entities, specifying that they are or were legally married to each other.
-
B.
neverMarried
Indicates that the subject has not been legally married to any partner at any time up to the present.
-
C.
marriedBefore
Indicates that one entity entered into a marriage at an earlier time than the other entity.
-
D.
hasMarriagePlot
Indicates that the work’s narrative centrally involves courtship, romantic relationships, or the progression toward marriage as a key plot element.
-
E.
marriedIn
Indicates that two entities entered into a marital relationship at a specific place or within a particular jurisdiction.
- 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_69b3454c772081908e20173e379e8ebe |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b351e47d388190b31500189577cd75 |
completed | March 12, 2026, 11:53 p.m. |
| PD | Predicate disambiguation | batch_69b34f53e3cc8190bf5d4dbe2413bf65 |
completed | March 12, 2026, 11:42 p.m. |
| PDg | Predicate description generation | batch_69b34ff654308190b9717526120d80d3 |
completed | March 12, 2026, 11:44 p.m. |
Created at: March 12, 2026, 11:16 p.m.