Triple
T22556090
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christopher Monck, 2nd Duke of Albemarle |
E557685
|
entity |
| Predicate | hadNoIssue |
P82927
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Christopher Monck, 2nd Duke of Albemarle, hadNoIssue, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadNoIssue Context triple: [Christopher Monck, 2nd Duke of Albemarle, hadNoIssue, true]
-
A.
hadIssue
Indicates that an entity experienced, encountered, or was affected by a particular problem, defect, or difficulty.
-
B.
hasNoIssue
chosen
Indicates that there are no problems, defects, or conflicts associated with the referenced entity or situation.
-
C.
hadNo
Indicates that one entity completely lacked or did not possess another entity, attribute, or relationship.
-
D.
hadMultipleIssues
Indicates that the subject experienced more than one problem, error, or issue in the relevant context.
-
E.
hadKeyIssue
Indicates that an entity experienced a primary or critical problem related to a key aspect, factor, or component.
- 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_69e11e59db848190b4272ecd2b690ffd |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15f7a4a3c81908fc87f48b6dcbbf7 |
completed | April 29, 2026, 1:31 a.m. |
| PD | Predicate disambiguation | batch_69e898cb3fb48190add6ab24a2df5822 |
completed | April 22, 2026, 9:45 a.m. |
Created at: April 16, 2026, 8:52 p.m.