Triple
T910879
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anne, Princess Royal |
E19653
|
entity |
| Predicate | startTime (marriage to Timothy Laurence) |
P198
|
FINISHED |
| Object | 1992 |
—
|
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: 1992 | Statement: [Anne, Princess Royal, startTime (marriage to Timothy Laurence), 1992]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: startTime (marriage to Timothy Laurence) Context triple: [Anne, Princess Royal, startTime (marriage to Timothy Laurence), 1992]
-
A.
spouseStartTime
Indicates the point in time when two individuals began their spousal (marriage) relationship.
-
B.
marriageDate
chosen
Indicates the specific date on which two entities entered into a marital relationship.
-
C.
ageAtMarriage
Indicates the age a person was when they got married.
-
D.
spouseRelationshipEnd
Indicates that a marital relationship between two individuals has ended, such as through divorce, annulment, or separation.
-
E.
marriageType
Indicates the specific legal or social category of a marriage relationship that exists between two spouses.
- 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_69a4939f91a08190ba68c2c81eab90fe |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b2f605bc8190a5245aa2ca55cf43 |
completed | March 1, 2026, 9:43 p.m. |
| PD | Predicate disambiguation | batch_69a4b2918ea881908698020b995a8eae |
completed | March 1, 2026, 9:41 p.m. |
Created at: March 1, 2026, 7:39 p.m.