Triple
T2800622
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | wedding of Prince Harry and Meghan Markle |
E53143
|
entity |
| Predicate | brideNationalityAtTimeOfWedding |
P43284
|
FINISHED |
| Object | American |
—
|
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: American | Statement: [wedding of Prince Harry and Meghan Markle, brideNationalityAtTimeOfWedding, American]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: brideNationalityAtTimeOfWedding Context triple: [wedding of Prince Harry and Meghan Markle, brideNationalityAtTimeOfWedding, American]
-
A.
marriageLocation
Indicates the place where a marriage ceremony or legal union between two people took place.
-
B.
spouseCountryOfCitizenship
Indicates the country in which a person's spouse holds legal citizenship.
-
C.
countryOfOrdination
Indicates the country in which an individual was formally ordained into a religious office or role.
-
D.
marriageType
Indicates the specific legal or social category of a marriage relationship that exists between two spouses.
-
E.
bearerNationality
Indicates that one entity is the country or nationality associated with the bearer of another entity, such as a document or credential.
- 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_69ab495a90788190941b6917e1eca3a6 |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abde2ec2ac8190bd702ad3eafb6aed |
completed | March 7, 2026, 8:13 a.m. |
| PD | Predicate disambiguation | batch_69abdd059f308190853191f6ffe2bc6f |
completed | March 7, 2026, 8:08 a.m. |
| PDg | Predicate description generation | batch_69abde2cdcc48190827195d3ae70aa19 |
completed | March 7, 2026, 8:13 a.m. |
Created at: March 6, 2026, 9:58 p.m.