Triple
T36145640
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Queen Victoria’s Golden Jubilee |
E1045439
|
entity |
| Predicate | marksReignYears |
P110672
|
FINISHED |
| Object | 1837–1887 |
—
|
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: 1837–1887 | Statement: [Queen Victoria’s Golden Jubilee, marksReignYears, 1837–1887]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marksReignYears Context triple: [Queen Victoria’s Golden Jubilee, marksReignYears, 1837–1887]
-
A.
yearOfReign
chosen
Indicates the specific year or span of years during which a ruler or authority held official power or reigned.
-
B.
numberOfReigns
Indicates the count of distinct reigns or ruling periods associated with an entity.
-
C.
reignDuration
Indicates the length of time during which an entity held ruling or governing authority over a domain or group.
-
D.
centuryOfReign
Indicates the century during which a given entity’s reign or rule took place.
-
E.
mostReignsNumber
Indicates that the subject holds the highest number of separate reigns or terms in a given role or title compared to all others.
- 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_69f76e37ace88190a906b107d388f5d1 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7b69b333081909cadbed3fcb8ecf5 |
completed | May 3, 2026, 8:56 p.m. |
| PD | Predicate disambiguation | batch_69f7b4c2a5f8819094ad4621d7b97e0c |
completed | May 3, 2026, 8:49 p.m. |
Created at: May 3, 2026, 4:08 p.m.