Triple
T2800623
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | wedding of Prince Harry and Meghan Markle |
E53143
|
entity |
| Predicate | brideProfessionBeforeMarriage |
P28984
|
FINISHED |
| Object | actress |
—
|
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: actress | Statement: [wedding of Prince Harry and Meghan Markle, brideProfessionBeforeMarriage, actress]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: brideProfessionBeforeMarriage Context triple: [wedding of Prince Harry and Meghan Markle, brideProfessionBeforeMarriage, actress]
-
A.
earlierOccupation
chosen
Indicates that one occupation held by an entity occurred before another occupation in that entity’s work history.
-
B.
marriedBefore
Indicates that one entity entered into a marriage at an earlier time than the other entity.
-
C.
spouseOccupation
Indicates that one person’s spouse has a particular job, profession, or occupation.
-
D.
positionOnMarriage
Indicates a person's stance, opinion, or policy regarding the institution or practice of marriage.
-
E.
subjectOccupation
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
- 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_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. |
Created at: March 6, 2026, 9:58 p.m.